Method for determining a three-dimensional representation of an object using points, and corresponding computer program and imaging system

ABSTRACT

The method of the invention includes: determining a set of points of a space and a value of each of these points at a given moment, the set of points including the points of the object in the position thereof at the given moment; selecting a three-dimensional representation function that can be parameterized with parameters and an operation that gives, using the three-dimensional representation function, a function for estimating the value of each point in the space; and determining parameters, such that, for each point in the set, the estimation of the value of the point substantially gives the value of the point.

The present invention relates to a method for determining a three-dimensional representation of an object.

The invention applies in particular for the reconstruction of micro objects in microscopic-imaging systems.

The prior art document EP 1 413 911 A1 describes a method for determining a three-dimensional representation of an object on the basis of a sequence of sectional images of the object in a section plane, each sectional image having been captured at a respective instant of picture capture while the object moved with respect to the section plane.

In this prior art document, the object is a cell of dimension of the order of a few micrometers. The cell is marked with a fluorescent compound and is placed in a receptacle of a microscopic-imaging system. A confinement field is created in the receptacle so as to control the position of the cell, without the latter being squashed. The microscopic-imaging system furthermore comprises an optical microscope having a focal plane forming the section plane. In order to obtain the sequence of sectional images, the cell is rotated at a constant angular rate around an axis of the focal plane, and sectional images are captured at a rate of between 1 and 1000 acquisitions per second.

The prior art document furthermore proposes to determine the three-dimensional representation of the cell on the basis of the usual reconstruction techniques used in tomography. These usual techniques consist in determining the three-dimensional representation of an object on the basis of sectional images and of the positions of the object with respect to the section plane, the positions of the object with respect to the section plane being determined by the knowledge of the adjustment settings of the radiography machine and/or of physical sensors making it possible to ascertain the position of the radiography machine.

The aim of the invention is to provide a reconstruction method allowing the fast determination of a three-dimensional representation while having good resolution.

Thus, the subject of the invention is a method for determining a three-dimensional representation of an object, characterized in that it comprises:

-   -   the determination of a set of points of a volume and of a value         of each of these points at a given instant, the set of points         comprising points of the object in its position at the given         instant,     -   the choosing of a three-dimensional representation function that         can be parametrized with parameters, and of an operation giving,         on the basis of the three-dimensional representation function,         an estimation function for the value of each point of the set,     -   the determination of parameters, such that, for each point of         the set, the estimation of the value of the point gives         substantially the value of the point.

According to other characteristics of the invention:

-   -   the three-dimensional representation function comprises a         decomposition into basis functions around nodes, so as to obtain         a sum of terms, each term comprising the basis function with a         variable dependent on a respective node associated with this         term,     -   the basis function comprises a product of B-spline functions in         each of the three directions in space,     -   the volume comprises a plurality of sub-volumes; the parameters         being distributed in groups of parameters, the three-dimensional         representation function is chosen so that each group of         parameters is associated with a respective sub-volume; the         determination of the parameters comprises, successively for each         sub-volume, the determination of the parameters associated with         this sub-volume, such that, for each point of the sub-volume,         and preferably also of the sub-volumes directly contiguous with         the sub-volume, the estimation of the value of the point gives         substantially the value of the point, the parameters associated         with the other sub-volumes being fixed at a given value,     -   the value of each point of the set is obtained on the basis of a         respective sectional image of the object, associated with the         point; the operation gives a function for estimating the value         of each point of the set, on the basis of the three-dimensional         representation function and of a point spread function, the         point spread function depending on a rotation between the         position of the object at the instant of capture of the         respective sectional image associated with the point, and the         position of the object at the given instant; the         three-dimensional representation function is chosen such that,         for each point of volume:

Op(φ,f _(R))(u)=Op(φ,f)(Ru),

with Op the operation, φ the basis function, R an arbitrary rotation, f_(R) the point spread function for the rotation R, f_(R)(u)=f(Ru), f the point spread function without rotation, and Ru the point resulting from the rotation of the point u by the rotation R,

-   -   the operation is a convolution of the three-dimensional         representation function with the point spread function,     -   the basis function is a radial basis function, each term         depending on the distance of each point with the node associated         with this term, but being independent of the direction between         the point and the node,     -   the determination of the set of points and of a value of each         point is carried out on the basis of several sequences of         sectional images, and the method comprises: the determination of         a three-dimensional representation function, on a respective         sub-volume, for each sequence, each three-dimensional         representation giving a representation of the object in a         respective position; the determination, for each sequence, of a         rotation and of a translation making it possible to         substantially place all the positions of the representations of         the object in a reference position,     -   the determination, for each sequence, of the rotation and of the         translation comprises: the selection, in each subset, of at         least three groups, preferably four or more, of points of the         subset, according to a selection criterion, which is the same         for all the sequences of sectional images; the determination of         the rotation and of the translation of each sequence of         sectional images on the basis of the groups of points,     -   the determination of the rotation and of the translation of each         sequence of sectional images on the basis of the groups of         points comprises: the calculation, for each sequence of         sectional images, of a barycenter of each of the groups of         points; the determination of the rotation and of the translation         of each sequence on the basis of the barycenters,     -   the method comprises: the determination of an interval of         acquisition of each sectional image; the determination of a         continuous motion of the object during the interval of         acquisition of each sectional image; the taking into account of         the continuous motion of the object during the acquisition         interval so as to determine the three-dimensional representation         of the object,     -   the motion of the object with respect to the section plane         comprises a motion of rotation about a fixed axis and with a         fixed angular rate and a series of perturbation translations,         each undergone by the object between two successive respective         sectional images; the continuous motion comprises the rotation         about the fixed axis and with the fixed angular rate during the         acquisition time for each sectional image, and a linear fraction         of the perturbation translation undergone by the object between         the sectional image and the next one.

The subject of the invention is also a computer program product which, when implemented on a computer, implements a method of the invention.

The subject of the invention is also an imaging system characterized in that it comprises: means making it possible to obtain images in a focal plane, a receptacle for receiving an object, means for setting the object into motion, means for receiving sectional images captured in the focal plane, which means are adapted for implementing a method according to the invention.

These characteristics, as well as others, will become apparent on reading the description which follows of preferred embodiments of the invention. This description is given with reference to the appended drawings, among which:

FIG. 1 represents an imaging system according to the invention,

FIG. 2 represents the steps of a method according to the invention for analyzing an object,

FIG. 3 represents the steps of a method, implemented in the analysis method of FIG. 2, for processing sectional images of the object,

FIG. 4 represents the steps of determining parameters of the motion of the object with respect to a section plane,

FIG. 5 represents the steps of determining, according to a first variant, an axis of rotation of the object and a series of perturbation translations that the object undergoes,

FIG. 6 illustrates the orthogonal projection of a sectional image on a support plane of a spatially neighboring image,

FIG. 7 represents the steps of determining, according to a second variant, an axis of rotation of the object and a series of perturbation translations that the object undergoes,

FIG. 8 represents the steps of determining, according to a third variant, an axis of rotation of the object and a series of perturbation translations that the object undergoes,

FIG. 9 illustrates a circle cutting the section plane, which a point of the object describes about the axis of rotation,

FIG. 10 represents the steps of the start of a determination of a three-dimensional representation of the object,

FIG. 11 represents the steps of the end, according to a first variant, of the determination of a three-dimensional representation of the object,

FIG. 12 represents the steps of the end, according to a second variant, of the determination of a three-dimensional representation of the object,

FIG. 13 represents the steps of a variant for determining a set of points, for the determination of the three-dimensional representation of the object of FIG. 10,

FIG. 14 represents the steps of a variant for selecting groups of points, carried out in the determination of the set of points of FIG. 13,

FIG. 15 represents the steps, according to a third variant, of determining a three-dimensional representation of the object, and

FIG. 16 represents the steps of an adjustment of the imaging system of FIG. 1.

The description which follows relates to the determination of a three-dimensional representation of a live cell. However, the person skilled in the art will have no hardship in transposing this description to other types of objects, live or inert.

Moreover, the mathematical relations indicated subsequently are expressed in a fixed XYZ frame, tied to the section plane P (which will be described further on) and whose XY plane coincides with this section plane P.

Furthermore, in the present description, the term “value” does not signify solely a number value, but can also be a vector value (that is to say a particular vector) or a line value (that is to say a particular line), etc., according to the nature of the mathematical object whose value is considered.

Description of the Imaging System

With reference to FIG. 1, a microscopic-imaging system 10 comprises first of all an optical microscope 12. The optical microscope 12 comprises a lens 14 defining a focal plane P. The optical microscope 12 furthermore comprises a camera 16, for example a CCD camera making it possible to obtain images in the focal plane P.

The microscopic-imaging system 10 furthermore comprises a receptacle 18 for receiving an object O of microscopic size, such as a cell, into which a fluorescent material has been introduced.

Generally, it is possible to replace the fluorescent material by any marker adapted to the object under study, and able to be detected by the imaging system used.

The receptacle 18 comprises a chamber 20 intended to contain a fluid microsystem comprising the object O. The chamber 20 is situated facing the lens 14 of the optical microscope 12. The chamber 20 is delimited by a support 24, lateral walls 26 and a glass pane 28 covering the walls 26 so that the lens can observe the content of the chamber 20. The chamber 20 defines a volume U.

The lateral walls comprise microelectrodes 30 for creating an electric field, the latter making it possible to position the object O.

The microscopic-imaging system 10 furthermore comprises a device 32 for illuminating the marker contained in the object O, so that each point o of the object O emits a luminosity O(o). Conversely, the environment of the object O emits a very low or even zero luminosity.

The microscopic-imaging system 10 also comprises a control unit 34, acting in particular on the microelectrodes 30 so as to set the object O into motion, and on the camera 16 so as to capture a sequence of sectional images X₀ . . . X_(m) in the focal plane P (which thus forms a section plane P of the volume U of the chamber 20, and in particular of the object O), at respective picture-capture instants t₀ . . . t_(m).

An XYZ reference frame is tied to the section plane P. Another reference frame, termed the reference frame of the object O, is furthermore tied to the object O. This other reference frame is chosen, preferably, such that it coincides with the XYZ reference frame at the initial instant t₀. Moreover, the term “a point of the object” subsequently signifies, unless explicitly indicated, a point in the reference frame of the object O.

Each sectional image X_(k) extends over a support plane P_(k) tied to the object O, this support plane P_(k) coinciding with the section plane P at the moment t_(k) of the capture of the sectional image X_(k).

Each sectional image X_(k) comprises a grid G of pixels s. The grid G is the same for all the sectional images X₀ . . . X_(m). Each pixel s records a value X_(k)(s) of the luminosity of the marker at the position of the pixel s in the volume U of the chamber 20. In the present description, this luminosity value is recorded in a monochrome manner, in the form of a gray level.

Thus, when a point o of the object O is situated at the position of the pixel s, at the instant t_(k) of capture of the sectional image X_(k), the value X_(k)(s) of this pixel s is dependent in particular on the luminosity O(o) of the point o. When no point of the object O is situated at the position of the pixel s, it is the luminosity of the “void” which is recorded (in fact, that of the fluid comprising the object O). Thus, the background of the sectional images X₀ . . . X_(m), has a low gray level.

Furthermore, the value X_(k)(s) of a pixel s also depends on a point spread function (PSF) introducing a fuzziness. In general, the point spread function has a shape which is elongated perpendicularly to the section plane P.

The microscopic-imaging system 10 moreover comprises an image processing computing device 36, linked to the control unit 30 so as to receive the sectional images X₀ . . . X_(m). A reconstruction computer program 38 is installed on the computing device 36. The computer program 38 is designed to implement a reconstruction method intended to determine a three-dimensional representation V of the object O on the basis of the sequence of sectional images X₀ . . . X_(m). The computing device 36 is able to export the three-dimensional representation V in the form of a digital file and/or to display this three-dimensional representation V on a screen 40.

Description of the Analysis Method

A method of analyzing an object O, implemented by the imaging system 10, is illustrated in FIG. 2. With reference to this FIG. 2, the method for analyzing an object O comprises a step 50 of introducing the object O into the chamber 20, and then a step 52 of configuring the control unit 34 so as to rotate the object O about a fixed axis of rotation L, at a fixed angular rate τ. The axis L is defined by a point u₀ on the axis of rotation L—subsequently called the point of passage u₀—and a direction ā of the axis of rotation L, with unit norm: ∥ā∥=1. The axis of rotation L is not perpendicular to the section plane P.

The analysis method furthermore comprises a step 54 of acquiring at least one sequence of sectional images X₀ . . . X_(m) at respective picture-capture instants t₀ . . . t_(m), and a step 56 of processing the sectional images X₀ . . . X_(m) with the computer program 38.

In practice, the axis of rotation L is never exactly that set by the control unit 34. The invention therefore proposes to determine the axis of rotation L on the basis of the sectional images X₀ . . . X_(m), and then to determine a three-dimensional representation V on the basis of the axis of rotation L determined, rather than on the basis of the axis of rotation set on the basis of the adjustment of the control unit 34.

Furthermore, in practice, the motion of the object O is never perfectly rotary. The motion error with respect to the rotation of fixed axis is represented by a series of perturbation translations T₁ . . . T_(m), each perturbation translation being undergone by the object O between two respective successive sectional images X_(k−1), X_(k). The perturbation translations T₁ . . . T_(m) have variable direction and value.

Thus, the position of a point o of the object O at a picture-capture instant t_(k), starting from the position u of the point o at the previous picture-capture instant t_(k−1), is:

R _(α,τ(t) _(k) _(−t) _(k−1) ₎(u−u₀)+u₀+T_(k),

where R _(α,τ(t) _(k) _(−t) _(k−1) ₎ is the rotation matrix of angle τ(t_(k)−t_(k−1)) about the axis with direction ā passing through the origin of the XYZ reference frame. It will be noted that the rotation matrix R _(α,α) of angle α about an axis with direction ā passing through the origin is given, according to Rodrigues' formula, by:

R _(α,α) =I+sin α[ α]_(k)+(1−cos α)[ α]_(k) ²,

where I is the 3-by-3 identity matrix, and

$\left\lbrack \overset{\rightarrow}{a} \right\rbrack_{x} = \begin{bmatrix} 0 & {- a_{3}} & a_{2} \\ a_{3} & 0 & {- a_{1}} \\ {- a_{2}} & a_{1} & 0 \end{bmatrix}$

with ā=(a₁,a₂,a₃).

It will also be noted that the perturbation translation T_(k) does not depend on the position of u₀ on the axis of rotation L.

Processing of the Sectional Images Acquired

With reference to FIG. 3, the processing step 56 implemented by the computer program 38 comprises first of all a step 100 of extending each sectional image X₀ . . . X_(m), in the course of which values of points between the pixels s are calculated, as are values of points outside the grid G. For example, the values of the points between the pixels s are calculated by interpolation or by smoothing on the basis of the values of the pixels s of the grid G, while the values of the points outside the grid G are set to a low gray level, for example 0. Hereinafter, an arbitrary point of a sectional image, a pixel whose value is measured or a point whose value is calculated, will be denoted x, while a pixel proper of a sectional image will be denoted s. The value of a point x will be denoted X_(k) (X), while the value of a pixel s will be denoted X_(k) (S).

Step 56 implemented by the computer program 38 furthermore comprises a step 200 of determining the parameters of the motion of the object O with respect to the section plane P. In the course of this step of determining the parameters of the motion 200, the parameters of regular motion (angular rate τ, the axis of rotation L) are determined, as are the perturbation motion parameters (series of perturbation translations T₁ . . . T_(m)).

The motion parameters (angular rate τ, the axis of rotation L and series of perturbation translations T₁ . . . T_(m)) determine the position of the object O at each instant of capture t_(k) of a sectional image X_(k): the position at the instant of capture t_(k) of a point o which is situated at the position u at the initial instant t₀ is given by:

R _(α,τ(t) _(k) _(−t) ₀ ₎(u−u₀)+u₀+ T _(k),

where T _(k)=R _(α,τ(t) _(k) _(t) ₁ ₎T₁+R _(α,τ(t) _(k) _(−t) ₂ ₎T₂+ . . . +R _(α,τ(t) _(k) _(−t) _(k−1) ₎T_(k−1)+T_(k), the cumulative translation aggregated from the initial instant t₀ to the instant t_(k) of capture of the sectional image X_(k), with T ₀=0.

The position of the object O at each instant of capture t_(k) of a sectional image X_(k) determines the position of the section plane P at the instant of capture t_(k) in an arbitrary reference frame tied to the object O (and vice-versa): the position in the reference frame of the object O chosen (which coincides with the fixed XYZ reference frame at the initial instant of capture t₀) of a pixel s of the section plane P at the instant of capture t_(k) is:

R _(α,τ(t) _(k) _(−t) ₀ ₎ ^(t)(π₃s−u₀− T _(k))+u₀

with R _(α,τ(t) _(k) _(−t) ₀ ₎ ^(t) the matrix transpose of R _(α,τ(t) _(k) _(−t) ₀ ₎,

${\pi_{3} = \begin{bmatrix} 1 & 0 \\ 0 & 1 \\ 0 & 0 \end{bmatrix}},$

π₃x is the position in the XYZ three-dimensional reference frame of a point x of a sectional image, when merging the support plane of the sectional image with the XY plane. The position of the section plane P in any other reference frame tied to the object O at each instant of capture t_(k) follows from the position of the section plane P in the reference frame of the object O chosen at the instant of capture t_(k) and from the relation between the reference frame of the object chosen and this other reference frame.

In the description which follows, the motion of the object O with respect to the section plane P is expressed in the XYZ reference frame tied to the section plane P. Quite obviously, the motion of the object O with respect to the section plane P could be expressed in another frame of reference, which would not necessarily be tied to the section plane. In this case, the determination of the motion of the object O with respect to the section plane P would furthermore comprise the determination of the motion of the section plane P with respect to this other frame of reference.

Step 56 implemented by the computer program 38 furthermore comprises a step 400 of determining a three-dimensional representation V of the object O, on the basis of the sectional images X₀ . . . X_(m), and of the motion parameters τ, L, T₁ . . . T_(m).

Determination of the Angular Rate in Absolute Value

The sign of the angular rate τ indicates the direction of the rotation of the motion. The sign of the angular rate τ is positive if the rotation occurs in the positive sense with respect to the direction ā, and negative if the rotation occurs in the negative sense with respect to the direction ā. The sign of the angular rate τ is known, for example, according to the adjustment of the imaging system 10, once the direction ā of the axis of rotation has been chosen.

If the sign of the angular rate τ is not known, it may be chosen arbitrarily: in this case, the three-dimensional representation V of the object O will at worst be the three-dimensional representation V of the mirror of the object O.

With reference to FIG. 4, step 200 of determining the parameters of the motion comprises a step 210 of determining the angular rate in absolute value |τ|.

This step 210 comprises first of all a step 212 of determining a period p>0 such that each pair of sectional images X_(k), X_(k′) captured at respective instants t_(k), t_(k′) separated by a time substantially equal to a (nonzero) multiple of the period p, are substantially similar. This period p is thus the period of revolution of the object O.

More precisely, step 212 of determining the period of revolution p comprises a step 214 of determining an initial group of positive candidate periods p₁ . . . p_(n), and a step 216 of selecting the period of revolution p from among the candidate periods p₁ . . . p_(n) of the initial group.

In the example described, the step 214 of determining the candidate periods p₁ . . . p_(n) consists in choosing the candidate periods p₁ . . . p_(n). Preferably, the candidate periods p₁ . . . p_(n) are chosen uniformly spaced.

Simple Determination of the Period

Step 216 of selecting the period of revolution p comprises, in a simple variant, the determination of the best period from among the candidate periods p₁ . . . p_(n), by maximizing the likelihood according to a chosen probabilistic model.

Enhanced Determination of the Period

However, to improve the reliability of the determination of the period of revolution p, one or more prior selections are carried out on the candidate periods p₁ . . . p_(n), the probabilistic model taking account of this or these selections.

With reference to FIG. 4, step 216 of selecting the period of revolution p comprises two steps 218, 220 of selection, so as to obtain, on the one hand, for each sectional image X_(k), a first respective subset p_(j(k,1)), . . . , p_(j(k,e)) of candidate periods and, on the other hand, for all the sectional images X₀ . . . X_(m), a second subset p_(j(1)) . . . p_((l)) of candidate periods.

Selection of the First Subsets

The first step 218 of selecting the first subsets p_(j(k,1)), . . . , p_(j(k,e)) comprises, for each sectional image X_(k) and for each candidate period p_(j), a step 222 of determining substantially periodic sectional images X_(k′) (according to the candidate period p_(j)) to the sectional image X_(k).

Step 222 of determining the substantially periodic sectional images X_(k′) comprises a step 224 of determining the sectional images X_(k′) captured at picture-capture instants t_(k′) separated from the instant t_(k) of capture of the sectional image X_(k) by a time which is close to a (nonzero) multiple of the candidate period p_(j). “Close” signifies that the difference between the close time and the (nonzero) multiple of the candidate period p_(j) lies in a time interval J comprising 0. Preferably, the time interval J is centered on 0. For example, J=[ζ,ζ] with C small with respect to each candidate period p_(j), with ζ≦p_(j)/3 for the set of candidate periods p_(j). As a variant, ζ varies as a function of the candidate period p_(j) by choosing for example ζ=p_(j)/10 for each candidate period p_(j).

The first step 218 of selecting the first subsets {p_(j(k,1)), . . . , p_(j(k,e))} furthermore comprises a step 226 of recentering each substantially periodic sectional image X_(k), with respect to the sectional image X_(k).

The recentering step 226 comprises first of all a step 228 of selecting luminous pixels, in the sectional image X_(k) and in the substantially periodic sectional image X_(k′). Preferably, the pixels selected are those whose gray level is greater than a predetermined threshold α, this threshold being for example the q-quantile of gray level of the sectional images X₀ . . . X_(m) (this signifying that the proportion of the pixels of the sectional images X₀ . . . X_(m) which have a gray level of less than or equal to α is substantially equal to q, and that of the pixels which have a gray level greater than α is substantially equal to 1−q), with q being equal for example to between 60% and 95%.

The recentering step 226 furthermore comprises a step 230 of calculating, on the one hand, a first center d(X_(k)) of the luminous points selected in the sectional image X_(k) and, on the other hand, the calculation of a second center d(X_(k′)) of the luminous points selected in the sectional image X_(k).

The center of an image X (X_(k) or X_(k′)) is given by:

${d(X)} = \frac{\sum\limits_{s}{1_{{X{(s)}} > \alpha}s}}{\sum\limits_{s}1_{{X{(s)}} > \alpha}}$

where l_(A>B) is the indicator function: l_(A<B)=1 if A>B, and l_(A>B)=0 otherwise. The recentering step 226 furthermore comprises the determination 232 of a shift d_(k,k′) between the centers of the luminous points of the sectional image X_(k) and of the substantially periodic sectional image X_(k′): d_(k,k′)=d(X_(k))−d(X_(k′)).

The recentering step 226 furthermore comprises a step 234 of translating the substantially periodic sectional image X_(k′) by the shift d_(k,k′) between the centers, so as to obtain a centered substantially periodic sectional image, denoted Trans(X_(k),d_(k,k′)). The centered substantially periodic sectional image Trans(X_(k′),d_(k,k′)) is calculated, for each pixel s, by:

Trans(X _(k′) ,d _(k,k′))(s)=X _(k′)(s−d _(k,k′)).

The first step 218 of selecting the first subsets p_(j(k,1)), . . . , p_(j(k,e)) furthermore comprises a step 236 of determining a distance T(k,k′) between the sectional image X_(k) and each centered substantially periodic sectional image Trans(X_(k′),d_(k,k′)). Preferably, the distance T(k,k′) is given by the following relation:

∀0≦k,k′≦m,T(k,k′)=χ(X _(k),Trans(X _(k′) ,d _(k,k′))),

with χ a distance function, χ(X,Y) measures the difference between two images X and Y. The distance function χ is for example a quadratic distance of the gray levels of the pixels between the two images, given by:

${\chi \left( {X,Y} \right)} = {\sum\limits_{s}{\left( {{X(s)} - {Y(s)}} \right)^{2}.}}$

The first step 218 of selecting the first subsets p_(j(k,1)), . . . , p_(j(k,e)) furthermore comprises a step 238 of determining a periodic similitude level sim(X_(k),p_(j)) of the sectional image X_(k) on the basis of the distances T(k,k′).

The periodic similitude level sim(X_(k),p_(j)) characterizes the level of similitude of the sectional image X_(k) with the substantially periodic sectional images X_(k′), for the candidate period p_(j). Preferably, step 238 of determining a periodic similitude level sim(X_(k),p_(j)) comprises a step of calculating the inverse of the similitude through the following relation:

${{sim}^{- 1}\left( {X_{k},p_{j}} \right)} = \left\{ \begin{matrix} {\sum\limits_{r \neq 0}{{v\left( {k,j,r} \right)}/{\sum\limits_{r \neq 0}{h\left( {k,j,r} \right)}}}} & {{{if}\mspace{14mu} {\sum\limits_{r \neq 0}{h\left( {k,j,r} \right)}}} > 0} \\ \infty & {{otherwise}.} \end{matrix} \right.$

with:

${{v\left( {k,j,r} \right)} = {\sum\limits_{i:{{- \zeta} \leq {t_{i} - {rp}_{j}} \leq \zeta}}{{w\left( {t_{i} - {rp}_{j}} \right)}{T\left( {k,i} \right)}}}},{{h\left( {k,j,r} \right)} = {\sum\limits_{i:{{- \zeta} \leq {t_{i} - {rp}_{j}} \leq \zeta}}{w\left( {t_{i} - {rp}_{j}} \right)}}},.$

where r is a nonzero integer, ζ is defined in the same manner as for step 224 of determining the substantially periodic sectional images, and w is a positive weighting function defined on the interval J=[−ζ,ζ]. Preferably, w is symmetric with respect to 0 (w(t)=w(−t)), and the high values of w are concentrated around 0. For example w(t)=exp(−ct²) with c a positive constant. The function w makes it possible to decrease the influence of the substantially periodic sectional images X_(k′) which are far distant from a multiple of the candidate period p_(j).

The first step 218 of selecting the first subsets p_(j(k,1)), . . . , p_(j(k,e)) furthermore comprises a step 240 of selecting, from among the candidate periods p₁ . . . p_(n), for each sectional image X_(k), a first subset p_(j(k,1)), . . . , p_(j(k,e)) grouping together the candidate periods p₁ . . . p_(n) having the highest similitude levels (that is to say the smallest values of sim⁻¹(X_(k),p_(j))). Preferably, a predetermined number e of candidate periods are selected. Preferably also, this number e is chosen between 1 and 15.

Selection of the Second Subset

The second step 220 of selecting the second subset p_(j(1)) . . . p_(j(l)) of candidate periods comprises, for each candidate period p_(j), a step 242 of calculating a number of appearances S(p_(j)) of the candidate period p_(j), corresponding to the number of first subsets p_(j(k,1)), . . . , p_(k(k,e)) in which the candidate period p_(j) appears.

The values of the number of appearances S for the candidate periods p₁ . . . p_(n) are for example given by the relation:

${\forall{1 \leq j \leq n}},{{S\left( p_{j} \right)} = {\sum\limits_{k = 0}^{m}1_{p_{j} \in {\{{p_{j{({k,1})}},\ldots \mspace{14mu},p_{j{({k,e})}}}\}}}}},$

where l_(AεB) is the indicator function: l_(AεB)=1 if AεB, and l_(AεB)=0 otherwise.

The second step 220 of selecting the second subset p_(j(1)) . . . p_(j(l)) of candidate periods furthermore comprises, for each candidate period p_(j), a step 244 of calculating a dispersion ℑ of the values of the number of appearance S, about each multiple (greater than or equal to 1) of the candidate period p_(j). The dispersions for a candidate period p_(j) indicates whether high values of the number of appearance S are concentrated (low dispersion) or dispersed (high dispersion) around each multiple (greater than or equal to 1) of the candidate period p_(j).

Preferably, the dispersion ℑ of a candidate period p_(j) is calculated in such a way that, the more distant another candidate period p_(j), is from the closest multiple (greater than or equal to 1) of the candidate period p_(j), the more the value of the number of appearance S of this other candidate period p_(j), contributes to the dispersion.

Preferably, the dispersion 3 of the candidate period p_(j) is given by the relation:

${\forall{1 \leq j \leq n}},{{\left( p_{j} \right)} = {\sum\limits_{i = 1}^{n}{\frac{{{\left\lbrack \frac{p_{i}}{p_{j}} \right\rbrack p_{j}} - p_{i}}}{p_{j}}{S\left( p_{i} \right)}}}},$

where [x] is the integer closest to x.

The second step 220 of selecting the second subset p_(j(1)) . . . p_((l)) of candidate periods furthermore comprises a step 246 of selecting, from among the candidate periods p_(j), of the second subset p_(j(1)) . . . p_(j(l)), candidate periods having the lowest dispersions ℑ. Preferably, a predetermined number l of candidate periods are selected. Preferably also, this number l is chosen between 4 and 40.

Selection of the Period on the Basis of the First Subsets, of the Second Subset, and of a Probability Law

The selection 216 of the period of revolution p furthermore comprises a step 248 of determining a probability law P_(p) describing the probability that a period p_(j) is chosen, in the course of step 218, in at least one of the first sets p_(j(k,1)), . . . p_(j(k,e)). The probability law P_(p) is defined with the period of revolution p as parameter.

The selection 216 of the period of revolution p furthermore comprises a step 249 of calculating a histogram h of the candidate periods selected in step 218. It has been noted that the large values of the histogram h are in general concentrated around the true value of the period of revolution p and multiples of the true value of the period of revolution p.

Preferably, it is considered that the selections for obtaining the candidate periods in step 218 are approximately independent, and that the choosing of each of the candidate periods in step 218 is carried out randomly according to a probability law P_(p).

For this purpose, the probability law P_(p), having as parameter the period of revolution p, is such that the shape of the probability function P_(p) can “hug” that of the histogram h to within an expansion factor, by varying the value of the parameter p.

More precisely, it is considered than each period p_(j) obtained in step 218 is selected either randomly, with a low probability β, from among all the candidate periods p₁ . . . p_(n) according to the uniform law, or with the probability 1−β through an alternative choice which consists in choosing a period around a multiple (greater than or equal to 1) of the period of revolution p. This alternative choice is carried out by choosing firstly a multiple ip (i an integer and i≧1) with a probability b_(i) and thereafter by choosing p_(j) around ip (ip already being chosen) with the probability v _(ip)(p_(j)).

In this case, the probability law P_(p) is defined by the relation:

${\forall{1 \leq j \leq n}},{{P_{p}\left( p_{j} \right)} = {\frac{\beta}{n} + {\left( {1 - \beta} \right){\sum\limits_{i = 1}^{l{(p)}}{b_{i}{{\overset{\_}{v}}_{ip}\left( p_{j} \right)}}}}}},$

where l(p) is the number of multiples of p.

The law P_(p) is a mixture of the uniform law on the set of candidate periods p₁ . . . p_(n), and of the laws v _(ip) with 1≦i≦l(p).

The probability v _(ip)(p_(j)) is preferably the translation of a support function v by the value ip: v_(ip)(x)=v(x −ip). Preferably, the support function v is chosen finite and positive so as to define b_(i) and v _(ip). Preferably also, v is symmetric about 0 (that is to say v(−x)=v(x)) and centered on 0. For example, we shall take v(x)∝e^(−dx) ² 1_(|x|≦δ), or else v(x)∝(δ−|x|)1_(|x|≦δ) for d and δ given positive constants.

Preferably l(p)=max{iεN*:{p₁, . . . , p_(n)}∩supp(v_(ip))≠Ø}, with supp(v_(ip))={x:v_(ip)(x)≠0}, and v _(ip)(p_(j))=v_(ip)(p_(j))/c_(i),

${b_{i} = \frac{c_{i}}{\sum\limits_{k = 1}^{l{(p)}}c_{k}}},{{{with}\mspace{14mu} c_{i}} = {\sum\limits_{k = 1}^{n}{{v_{ip}\left( p_{k} \right)}.}}}$

In practice β is chosen between 0 and 25%.

The selecting 216 of the period of revolution p furthermore comprises a step 250 of determining the period of revolution p, as being the period of the second subset of candidate periods p_(j(1)) . . . p_(j(l)) which maximizes the likelihood Like (p) (or, equivalently, the log-likelihood log Like (p)) associated with the above probability law P_(p), given the first subsets of selected periods p_(j(k,1)), . . . , p_(j(k,e)), 0≦k≦m. The log-likelihood is given by:

${\log \; {{Like}(p)}} = {\sum\limits_{j = 1}^{n}{{S\left( p_{j} \right)}\log \; {{P_{p}\left( p_{j} \right)}.}}}$

As a variant, step 212 of determining the period of revolution p does not comprise step 220 of determining the second subset, and, in step 250, the period of revolution p is determined as being the period of the set of candidate periods p₁ . . . p_(n), maximizing the above likelihood Like (p).

Step 210 of determining the angular rate in absolute value |τ| then comprises a step 252 of calculating the angular rate in absolute value |τ| on the basis of the period of revolution p:

|τ|=2π/p.

Once the direction ā of the axis of rotation has been chosen, the angular rate τ is determined from the known (or assumed) direction of rotation with respect to the direction ā of the axis of rotation, through the following relation:

$\tau = \left\{ \begin{matrix} {2{\pi/p}} & {{for}\mspace{14mu} {positive}\mspace{14mu} {rotations}} \\ {{- 2}{\pi/p}} & {{for}\mspace{14mu} {negative}\mspace{14mu} {{rotations}.}} \end{matrix} \right.$

Step 200 of determining the parameters of the motion furthermore comprises a step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m).

First Variant for Determining the Axis of Rotation and the Series of Perturbation Translations

FIG. 5 illustrates a first variant of step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m).

Step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) is implemented by determining an axis of rotation L and a series of perturbation translations T₁ . . . T_(m) such that the value X_(k) (s) of each pixel s of each sectional image X_(k) is substantially equal, for each spatially neighboring sectional image X_(k′) (that is to say whose support plane P_(k′) is spatially neighboring the support plane P_(k) of the sectional image X_(k)), to the value X_(k′)(x′) of a point x′ “close” to this pixel s, on the spatially neighboring sectional image X_(k′). The closest point x′ is determined according to a given proximity criterion.

More precisely, step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) comprises a step 304 of determining, for each sectional image X_(k), sectional images X_(k′) spatially neighboring this sectional image X_(k).

A sectional image X_(k′) is spatially neighboring the sectional image X_(k), when the angle separating their respective support planes P_(k), P_(k′) is less than a predetermined value Δ_(l). The angle is preferably determined on the basis of the angular rate in absolute value |τ| and of the instants t_(k), t_(k′) of capture of the two sectional images X_(k), X_(k′). This predetermined value Δ₁ is preferably taken less than or equal to 12 degrees. For a sectional image X_(k), the spatially neighboring sectional images X_(k′) are therefore the sectional images satisfying:

there exists an integer q such that |τ|(t _(k) −t _(k′))=2πq+r, with |r|≦Δ ₁.

The previous condition on the angle separating the sectional images X_(k) and X_(k′) is equivalent to a condition on the time interval separating the captures of the two sectional images.

Step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) furthermore comprises a step 306 of choosing a proximity criterion.

With reference to FIG. 6, preferably, this proximity criterion is that the point x′ of a spatially neighboring sectional image X_(k′), closest to a pixel s of the sectional image X_(k), is the position Proj_(k,k′)(s)=x′ of the orthogonal projection o′ of a point o of the object O on the support plane P_(k) of the sectional image X_(k), situated on the pixel s of the sectional image X_(k), on the support plane P_(k′) of the spatially neighboring image X_(k′).

This choice of this proximity criterion is particularly suitable in the case of a point spread function having an elongate shape perpendicularly to the section plane P.

Thus, returning to FIG. 5, step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) furthermore comprises a step 308 of calculating the orthogonal projection F_(k,k′)(X_(k)) of the sectional image X_(k) on the support plane P_(k′) of each spatially neighboring sectional image X_(k′) of the sectional image X_(k).

Preferably, step 308 of calculating the orthogonal projection F_(k,k′)(X_(k)) is carried out by calculating an affine transformation Aff(Q_(k,k′),v_(k,k′),X_(k)) of the sectional image X_(k), the affine transformation Aff(Q_(k,k′),v_(k,k′),X_(k)) having a linear transformation component Q_(k,k′)(ā,τ) and a translation component v_(k,k′)(u₀,ā,τ,T₁ . . . T_(m)) which are dependent on, respectively, on the one hand, the direction ā of the value of axis of rotation and the rotation rate τ and, on the other hand, the value of axis of rotation L, the rotation rate τ and the value of series of perturbation translations T₁ . . . T_(m). Thus:

F _(k,k′)(X)=Aff(Q _(k,k′)(ā,τ),v _(k,k′)(u ₀ ,ā,τ,T ₁ . . . T _(m)),X),

with Aff(Q,v,X) the affine transform of X defined by:

∀x,Aff(Q,v,X)(x)=X(Q ⁻¹(x−v))

with Q an invertible 2×2 matrix and v a translation vector, and

Q _(i,j)(ā,τ)=π₂ R _(ā,τ(t) _(j) _(−t) _(i) ₎π₃,

v _(i,j)(u ₀ ,ā,τ,T ₁ . . . T _(m))=(π₂−π₂ R _(ā,τ(t) _(j) _(−t) _(i) ₎)u ₀+π₂ T _(j)−π₂ R _(ā,τ(t) _(j) _(−t) _(i) ₎ T _(i),

where

$\pi_{2} = \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \end{bmatrix}$

is the projection on the XY plane,

${\pi_{3} = \begin{bmatrix} 1 & 0 \\ 0 & 1 \\ 0 & 0 \end{bmatrix}},$

π₃x is the position in the XYZ three-dimensional reference frame of a point x of a sectional image, when merging the support plane of the sectional image with the XY plane, and T _(k)=R_(ā,τ(t) _(k) _(−t) ₁ ₎T₁+R_(ā,τ(t) _(k) _(−t) ₂ ₎T₂+ . . . +R_(ā,τ(t) _(k) _(−t) _(k−1) ₎T_(k−1)+T_(k), the aggregated translation from the initial instant t₀ to the instant t_(k) of capture of the sectional image X_(k) with T ₀=0, and it is noted that for each possible direction ā of the axis of rotation, the sign of the angular rate τ is determined from the known (or assumed) direction of rotation with respect to the direction ā of the axis of rotation, for example according to the adjustment of the imaging system 10, through the following relation:

$\tau = \left\{ \begin{matrix} {\tau } & {{for}\mspace{14mu} {positive}\mspace{14mu} {rotations}} \\ {- {\tau }} & {{{for}\mspace{14mu} {negative}\mspace{14mu} {rotations}},} \end{matrix} \right.$

(if the direction of rotation is chosen arbitrarily: in this case, the three-dimensional representation V of the object O will at worst be the three-dimensional representation V of the mirror of the object O).

Step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) furthermore comprises a step 310 of comparing the values X_(k)(s) of the pixels s of each sectional image X_(k) with the values X_(k′)(x′) of the points x′ closest to the spatially neighboring sectional images X_(k′). Preferably, the comparison step 310 is carried out by calculating a distance χ(Aff(Q_(k,k′), v_(k,k′), X_(k)), X_(k′)) between the affine transform Aff(Q_(k,k′), v_(k,k′), X_(k)) of the sectional image X_(k) and the neighboring sectional image X_(k′).

Step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) furthermore comprises a step 312 of determining an axis of rotation L and a series of perturbation translations T₁ . . . T_(m) whose values minimize a cost function E comprising a first part dependent on the calculated distances, so that the reduction in the calculated distances gives rise to the reduction in the cost function E.

Preferably, the cost function E also comprises a second part which depends, for a series of perturbation translations T₁ . . . T_(m), on the amplitude of the translations of the series of perturbation translations T₁ . . . T_(m). This second part thus comprises a regularization function Reg, which gives a low value when the translations of the series of perturbation translations T₁ . . . T_(m) have a low amplitude, and gives a high value in the converse case. The regularization function Reg is preferably a quadratic norm, for example given by the relation:

Reg(T ₁ . . . T _(m))=∥T∥ _(M) ² ≡T ^(t) MT,

with M a symmetric positive definite matrix, and T=T₁ . . . T_(m) may be written in the form of a column vector with the translations T_(k) one after the other. For example, M=I_(3m), the 3 m×3 m identity matrix.

Preferably, the cost function E is given by the following relation:

${{E\left( {\overset{\rightarrow}{a},x_{0},{T_{1}\mspace{14mu} \ldots \mspace{14mu} T_{m}}} \right)} = {{\sum\limits_{k}{\sum\limits_{k^{\prime} \in N_{k}}{\chi \left( {{F_{k,k^{\prime}}\left( X_{k} \right)},X_{k^{\prime}}} \right)}}} + {\lambda \; {{Reg}\left( {T_{1}\mspace{14mu} \ldots \mspace{14mu} T_{m}} \right)}}}},$

with N_(k) the set of indices of the sectional images spatially neighboring the sectional image X_(k), χ(F_(k,k′)(X_(k)), X_(k′)) a measure of the distance between, on the one hand, the projection F_(k,k′)(X_(k)) of the sectional image X_(k) on the support plane P_(k′) of the spatially neighboring sectional image X_(k′) and, on the other hand, the spatially neighboring sectional image X_(k′), and λ≧0 is a parameter of compromise between, on the one hand, the distances between the projections F_(k,k′)(X_(k)) of the sectional images X_(k) and the spatially neighboring sectional images X_(k′) of the sectional, images X_(k), and, on the other hand, the regularization of translations T₁ . . . T_(m).

Second Variant for Determining the Axis of Rotation and the Series of Perturbation Translations

A second variant of step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) is represented in FIG. 7. This second variant of step 300 employs certain steps of the first variant of step 300.

With reference to this figure, step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) comprises, the angular rate in absolute value |τ| being known, a step 314 of determining the direction ā of the axis of rotation L, before a step 326 of determining a point of passage u₀ of the axis of rotation L and of the series of perturbation translations T₁ . . . T_(m).

Determination of the Direction of the Axis of Rotation

Step 314 of determining the direction ā of the axis of rotation L comprises a step 316, identical to the previous step 304, of determining, for each sectional image X_(k), the spatially neighboring sectional images X_(k′) of this sectional image X_(k).

Step 314 of determining the direction ā of the axis of rotation L furthermore comprises a step 318, identical to the previous step 306, of choosing a proximity criterion, according to which the point x′ of a spatially neighboring sectional image X_(k′), closest to a pixel s of the sectional image X_(k), is the position Proj_(k,k′)(s) of the orthogonal projection o′ of the point of the object o merged with the pixel s of the sectional image X_(k), on the support plane P_(k′) of the spatially neighboring image X_(k′).

Thus, step 314 of determining the direction ā of the axis of rotation L furthermore comprises a step 320 of calculating the orthogonal projection F_(k,k′)(X_(k)) of the sectional image X_(k) on the support plane P_(k′) of each spatially neighboring sectional image X_(k′) of the sectional image X_(k).

Preferably, step 320 of calculating the orthogonal projection F_(k,k′)(X_(k)) is carried out by calculating an affine transformation Aff(Q_(k,k′),v_(k,k′),X_(k)) of the sectional image X_(k), the affine transformation Aff(Q_(k,k′),v_(k,k′),X_(k)) having a linear transformation component Q_(k,k′)(ā,τ) and a translation component v_(k,k′). The linear transformation component Q_(k,k′)(ā,τ) is a function of a direction {right arrow over (a)} of axis of rotation and of the rotation rate τ, while, unlike in the first variant, the translation component v_(k,k′) is considered to be a variable, and is therefore not expressed as a function of the motion parameters of the object O. The family of translation vectors in the affine transformations F_(k,k′) will be denoted hereinafter v=(v_(k,k′))_(0≦k≦m,k′εN) _(k) . The orthogonal projection F_(k,k′)(X_(k)) is therefore expressed in the following manner:

F _(k,k′)(X)=Aff(Q _(k,k′)(ā,τ),v _(k,k′) ,X),

with Aff(Q,v,X) the affine transform of X defined by:

∀x,Aff(Q,v,X)(x)=X(Q ⁻¹(x−v))

with Q an invertible 2×2 matrix and v a translation vector, and

Q _(i,j)(ā,τ)=π₂ R _(ā,τ(t) _(j) _(−t) _(i) ₎π₃,

where

$\pi_{2} = \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \end{bmatrix}$

is the projection on the XY plane,

${\pi_{3} = \begin{bmatrix} 1 & 0 \\ 0 & 1 \\ 0 & 0 \end{bmatrix}},$

π₃x is the position in the XYZ three-dimensional reference frame of a point x of a sectional image, when merging the support plane of the sectional image with the XY plane, and it is noted that for each possible direction ā of the axis of rotation, the sign of the angular rate τ is determined from the known (or assumed) direction of rotation with respect to the direction ā of the axis of rotation, for example according to the adjustment of the imaging system 10, by the following relation:

$\tau = \left\{ \begin{matrix} {\tau } & {{for}\mspace{14mu} {positive}\mspace{14mu} {rotations}} \\ {- {\tau }} & {{{for}\mspace{14mu} {negative}\mspace{14mu} {rotations}},} \end{matrix} \right.$

(if the direction of rotation is chosen arbitrarily: in this case, the three-dimensional representation V of the object O will at worst be the three-dimensional representation V of the mirror of the object O).

Step 314 of determining the direction ā of the axis of rotation L furthermore comprises a step 322 of comparing the values X_(k)(s) of the pixels s of each sectional image X_(k) with the values X_(k′)(x′) of the points x′ closest to the spatially neighboring sectional images X_(k′). Preferably, the comparison step 322 is carried out by calculating a distance χ(Aff(Q_(k,k′),v_(k,k′),X_(k)), X_(k′)) between the affine transform Aff(Q_(k,k′),v_(k,k′),X_(k)) of the sectional image X_(k) and the neighboring sectional image X_(k′).

Step 314 of determining the direction ā of the axis of rotation L furthermore comprises a step 324 of minimizing a cost function U, in which the translation components v_(k,k′) are not expressed as a function of the parameters of the motion, but are left as variables of the cost function U.

Step 324 of minimizing the cost function U therefore amounts to finding the direction ā of axis of rotation and the family of translation components v_(k,k′) which minimize the cost function U.

Preferably, the cost function U is given by the relation:

${{U\left( {\overset{\rightarrow}{a},v} \right)} = {\sum\limits_{k}{\sum\limits_{k^{\prime} \in N_{k}}{\chi \left( {{{Aff}\left( {{Q_{k,k^{\prime}}\left( {\overset{\rightarrow}{a},\tau} \right)},v_{k,k^{\prime}},X_{k}} \right)},X_{k^{\prime}}} \right)}}}},$

with N_(k) the set of indices of the sectional images neighboring the sectional image X_(k), χ(Aff(Q_(k,k′)(ā,τ),v_(k,k′),X_(k)), X_(k′)) a measure of the distance between the affine transform of the sectional image X_(k) and the spatially neighboring sectional image X_(k′).

In the previous definition of the cost function U, the translation components v_(k,k′) are variables independent of u₀,ā,τ,T₁ . . . T_(m).

Determination of a Point of Passage of the Axis of Rotation and of the Series of Perturbation Translations

The direction ā of the axis of rotation L and of the translation components v_(k,k′), subsequently called the reference translation components v_(k,k′), having been determined, step 326 of determining a point of passage u₀ of the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) comprises a step 328 of expressing translation components v_(k,k′)(u₀,ā,τ,T₁ . . . T_(m)) as a function of a series of perturbation translations T₁ . . . T_(m) and of a point of passage u₀, the direction ā of the axis of rotation L and the rotation rate τ being known:

v _(k,k′)(u ₀ ,ā,τ,T ₁ . . . T_(m))=(π₂−π₂ R _(ā,τ(t) _(k′) _(−t) _(k) ₎)u ₀+π₂ T _(k′)−π₂ R _(ā,τ(t) _(k′) _(−t) _(k) ₎ T _(k),

where T _(k)=R_(ā,τ(t) _(k′) _(−t) ₁ ₎T₁+R_(ā,τ)t) _(k) _(−t) ₂ ₎T₂+ . . . +R_(ā,τ(t) _(k) _(−t) _(k−1) +T_(k), the aggregated translation from the initial instant t₀ to the instant t_(k) of capture of the sectional image X_(k) with T ₀=0.

Step 326 of determining a point of passage u₀ of the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) furthermore comprises a step 330 of determining the series of perturbation translations T₁ . . . T_(m) and the point of passage u₀, such that the translation components v_(k,k′)(u₀,ā,τ,T₁ . . . T_(m)) approximate the reference translation components v_(k,k′).

Preferably, step 330 of determining the series of perturbation translations T₁ . . . T_(m) and the point of passage u₀ comprises a step 332 of minimizing a cost function K which comprises a first part representing, for a series of perturbation translations T₁ . . . T_(m) and a point of passage u₀, a distance between the reference translation components v_(k,k′) and the translation components v_(k,k′)(u₀,ā,τ,T₁ . . . T_(m)) expressed as a function of the series of perturbation translations T₁ . . . T_(m) and of the point of passage u₀.

Preferably also, the cost function K comprises a second part representing the regularization Reg of the value T₁ . . . T_(m) of series of perturbation translations whose minimization reduces the amplitude of the translations T₁ . . . T_(m). The regularization function Reg is preferably a quadratic norm, for example given by the relation:

Reg(T ₁ . . . T _(m))=∥T∥ _(M) ² ≡T ^(t) MT,

with M a symmetric positive definite matrix, and T=T₁ . . . T_(m) may be written in the form of a column vector with the translations T_(k) one after the other. For example, M=I_(3m), the 3 m×3 m identity matrix.

Preferably, the cost function K is given by the relation:

${{K\left( {x_{0},{T_{1}\mspace{14mu} \ldots \mspace{14mu} T_{m}}} \right)} = {{\sum\limits_{k}{\sum\limits_{k^{\prime} \in N_{k}}{{v_{k,k^{\prime}} - {v_{k,k^{\prime}}\left( {\overset{\rightarrow}{a},\tau,{T_{1}\mspace{14mu} \ldots \mspace{14mu} T_{m}},x_{0}} \right)}}}^{2}}} + {\alpha \; {{Reg}\left( {T_{1}\mspace{14mu} \ldots \mspace{14mu} T_{m}} \right)}}}},$

with α≧0 a compromise between the quadratic discrepancy between v_(k,k′) and v_(k,k′)(u₀,ā,τ,T₁ . . . T_(m)), and the regularization of T₁ . . . T_(m) to control the amplitude of the translations T₁ . . . T_(m).

Third Variant for Determining the Axis of Rotation and the Series of Perturbation Translations

A third variant of step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) is represented in FIG. 8.

With reference to this FIG. 8, step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) comprises a step 334 of determining the direction ā of the axis of rotation L, in the course of which the perturbation translations T₁ . . . T_(m) are considered to be negligible (thereby signifying that the motion of the object O is considered to be a stable rotation about the axis of rotation L),

Determination of the Direction of the Axis of Rotation

Step 334 of determining the direction ā comprises a step 336 of determining a projection L_(XY) of the axis of rotation L on the section plane P, on the basis of the sectional images X₀ . . . X_(m).

Determination of a Projection of the Axis of Rotation on the Section Plane

The procedure for determining the projection L_(XY) uses the fact that each pixel s and its symmetric point L_(XY)(s) on the section plane P with respect to the projection L_(XY) have histograms h_(s) and h_(L) _(XY) _(9s)) which are close. This is due to the fact that, in the course of a rotation of the object O, the pixel s and its symmetric point L_(XY)(s) take substantially the same values, with simply a time shift corresponding to the time taken by the point o of the object O to move from the pixel s to its symmetric point L_(XY)(s).

In the course of step 336, the projection L_(XY) of the axis of rotation L on the section plane P is determined by choosing the line l_(XY) of the section plane P having the highest level of symmetry of histograms, that is to say, such that the histograms h_(s) and h_(l) _(XY) _((s)) of each pixel s and of its symmetric point l_(XY)(s) are close.

Thus, step 336 of determining a projection L_(XY) comprises, for each pixel s of the grid G, a step 338 of calculating a histogram h_(s) of the gray levels taken by this pixel s in at least a part of the sectional images X₀, . . . , X_(m). The histogram h_(s) represents the countdown of the values of the pixel s, without taking account of the order in which these values appear in the sequence of sectional images X₀, . . . , X_(m).

Preferably, the part of sectional images corresponds to the sectional images X₀, . . . , X_(m′) (with m′≦m) acquired while the object O carries out an integer number of rotations about the axis of rotation L. This results in:

${t_{m^{\prime}} - t_{0}} \approx {\frac{2\pi \; r}{\tau }\mspace{14mu} {for}\mspace{14mu} {an}\mspace{14mu} {integer}\mspace{14mu} {number}\mspace{14mu} {r.}}$

Step 336 of determining a projection L_(XY) furthermore comprises a step 340 of determining, for each pixel s of the grid G and for a line l_(XY) of the section plane P, a histogram h_(l) _(XY) _((s)) of the gray levels taken by the symmetric point l_(XY)(s) of this pixel s in the previous part of the sectional images.

Step 336 of determining a projection L_(XY) furthermore comprises a step 342 of determining the distances Δ(h_(s), h_(l) _(XY) _((s))) between the histogram h_(s) of each pixel s and the histogram h_(l) _(XY) _((s)) of the symmetric point l_(XY)(s). The distance Δ is for example the distance of Kolmogorov-Smirnov type:

${{\Delta \left( {h,h^{\prime}} \right)} = {\sup\limits_{x}{{{\overset{\_}{h}(x)} - {{\overset{\_}{h}}^{\prime}(x)}}}}},{{{with}\mspace{14mu} {\overset{\_}{h}(x)}} = {\sum\limits_{y \leq x}{{h(y)}.}}}$

Step 336 of determining a projection L_(XY) furthermore comprises a step 344 of minimizing a cost function Ψ, which represents, for a line l_(XY) of the section plane P, the differences between the histogram h_(s) of each pixel s and the histogram h_(l) _(XY) _((s)) of the symmetric point l_(XY)(s) with respect to the line l_(XY). Preferably, the cost function Ψ is given by the relation:

${\Psi \left( l_{XY} \right)} = {\sum\limits_{s \in G}{{\Delta \left( {h_{s},h_{l_{XY}{(s)}}} \right)}.}}$

L_(XY) is determined as being the line l_(XY) on the XY plane which minimizes the cost function Ψ.

Determination of the Angle Between the Axis of Rotation and its Projection

Step 334 of determining the direction ā furthermore comprises a step 346 of determining the angle between the axis of rotation L and its projection L_(XY) on the section plane P. This angle between the axis of rotation L and its projection L_(XY) is determined by calculating the angle θ between the Z axis of the XYZ reference frame (perpendicular to the section plane P) and the axis of rotation L.

With reference to FIG. 9, step 346 of determining the angle θ uses the fact that points o of the object O describe, in the course of time, a respective circle, centered substantially on the axis of rotation L, this circle cutting the section plane P, on the one hand, at a pixel s and, on the other hand, at its symmetric point L_(XY)(s) with respect to the projection L_(XY).

Returning to FIG. 8, step 346 of determining the angle θ comprises a step 348 of determining, for each pixel s of the grid G, a time t(s), termed the symmetrization time. The symmetrization time t(s) of a pixel s is the time required for a point o of the object O to move from this pixel s to the symmetric point L_(XY)(s) with respect to the projection L_(XY).

This results in the fact that the value X_(k)(s) of a pixel s on a sectional image X_(k) captured at an instant t_(k) is substantially equal to the value X_(k′)(L_(XY)(s)) of the symmetric point L_(XY)(s) on a sectional image X_(k′) captured at an instant t_(k′) shifted substantially by the symmetrization time t(s) with respect to the instant of capture t_(k).

Thus, the symmetrization time t(s) is preferably determined by determining the best temporal offset between the two vectors (X₀(s), X₁(s), . . . , X_(m)(s)) and (X₀(L_(XY)(s)),X₁(L_(XY)(s)), . . . , X_(m)(L_(XY)(s))). Thus, the vector offset by time μ of the vector (X₀(L_(XY)(s)),X₁(L_(XY)(s)), . . . , X_(m)(L_(XY)(s))) is defined by v(μ)=(X_(q(t) ₀ _(+μ))(L_(XY)(s)),X_(q(t) ₁ _(+μ))(L_(XY)(s)), . . . , X_(q(t) _(j(μ)) _(+μ))(L_(XY)(s))), with j(μ) such that j(μ)+1 is the number of images captured between the instant t₀ and the instant t_(m)−μ, and q(t) such that the instant of capture t_(q(t)) is the instant closest to the instant t from among all the instants of capture of sectional images. Preferably, the offset error is given by

Err(μ)=κ((X ₀(s),X ₁(s), . . . , X _(j(μ))(s)),v(μ)),

with κ(u,v) a measure of distance between two vectors u and v, κ is chosen for example as being a normalized quadratic distance:

${\kappa \left( {u,v} \right)} = {\frac{1}{l}{\sum\limits_{i}\left( {u_{i} - v_{i}} \right)^{2}}}$

with l the length of the vectors. Thus t(s) is obtained by minimizing the offset error Err (μ):

${{t(s)} = {\arg \; {\min\limits_{0 \leq \mu < p}{{Err}(\mu)}}}},$

with p=2π/|τ| the period of revolution.

Step 346 of determining the angle θ furthermore comprises a step 350 of determining the angle θ on the basis of the symmetrization times t(s).

Step 350 of determining the angle θ on the basis of the symmetrization times t(s) comprises a step 352 of determining a direction b of the projection L_(XY), with ∥ b∥=1, as well as a point of passage y₀ of the projection L_(XY). The projection L_(XY) is determined by its direction and the point of passage (y₀, b). The direction b of the projection L_(XY) is chosen as being, to within a multipartite positive constant, the projection on the XY plane of the direction of the axis of rotation ā that is determined subsequently:

b=π _(XY)(ā)/∥π_(XY)(ā)∥,

with

$\pi_{XY} = \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 0 \end{bmatrix}$

the matrix of projection on the XY plane. Thus it is assumed that the direction of rotation of the object O with respect to the direction of the axis of rotation ā which is determined subsequently, is known (or assumed), for example according to the adjustment of the imaging system 10, once the direction b has been chosen (such that b=π_(XY)(ā)/∥π_(XY)(ā)∥). Thus the angular rate τ of rotation is determined:

$\tau = \left\{ \begin{matrix} {\tau } & {{for}\mspace{14mu} {positive}\mspace{14mu} {rotations}} \\ {- {\tau }} & {{for}\mspace{14mu} {negative}\mspace{14mu} {{rotations}.}} \end{matrix} \right.$

If the direction of rotation of the object is unknown, it is chosen arbitrarily: in this case, the three-dimensional representation V of the object O will at worst be the three-dimensional representation V of the mirror of the object O.

Step 350 of determining the angle φ on the basis of the symmetrization times t(s) furthermore comprises a step 354 of determining, for each pixel s, a distance z(s) between, on the one hand, the middle

$\frac{s + {L_{XY}(s)}}{2}$

of the segment between the pixel s and its symmetric counterpart L_(XY)(s) with respect to the projection L_(XY), and, on the other hand, the center c(s) of the circle about which rotates a point o of the object O passing through the pixel s. This distance is positive if the center c(s) is above the XY plane, it is zero if c(s) is in the XY plane, and negative otherwise.

Preferably, the distance z(s) is given by the relation:

${{z(s)} = {{d(s)}\frac{{S - {L_{xy}(s)}}}{2{\tan\left( \frac{{t(s)}\tau}{2} \right)}}}},$

with t(s)τ the angle of the arc of the circle from s to the symmetric point L_(XY)(s) in the direction of rotation,

${d(s)} = \left\{ \begin{matrix} 1 & {{{if}\mspace{14mu} s} \in P^{R}} \\ {- 1} & {{{{if}\mspace{14mu} s} \in P^{L}},} \end{matrix} \right.$

P^(R) and P^(L) being the two half-planes of the section plane P, separated by the projection L_(XY). P^(R) and P^(L) are defined by:

P ^(L) ={sεP:< b

(s−y ₀), e ₃>≧0}

P ^(R) ={sεP:< b

(s−y ₀), e ₃><0},

with

the vector product, <.,.> the scalar product, and

${\overset{\rightarrow}{e}}_{3} = \begin{bmatrix} 0 \\ 0 \\ 1 \end{bmatrix}$

the direction of the Z axis of the reference frame. P^(R) and P^(L) do not depend on the position of y₀ on the projection L_(XY).

In theory, there is an affine relation between the distance z(s) and the position on projection L_(XY) of the axis of rotation, of the point

$\frac{s + {L_{XY}(s)}}{2}$

(which is also the projection of s on the projection L_(XY) of the axis of rotation) with respect to the point y₀:

z(s)=<s−y ₀ , b >cos φ+z ₀,

with z₀≧0 if the projection of the point y₀ on the axis of rotation L is above the XY plane or in the XY plane and z₀<0 otherwise, and |z₀| is the distance between y₀ and the axis of rotation L.

Thus, preferably, step 350 of determining the angle θ on the basis of the symmetrization times t(s) furthermore comprises a step 356 of determining the angle θ through the regression of the previous affine relation:

${\left( {\varphi,z_{0}} \right) = {\arg \; {\min\limits_{\hat{\varphi},{\hat{z}}_{0}}{\sum\limits_{s \in G_{\delta,\sigma_{\min}}}\left( {{z(s)} - {{\langle{{s - {\hat{y}}_{0}},\overset{\rightarrow}{b}}\rangle}\cos \; \hat{\varphi}} - {\hat{z}}_{0}} \right)^{2}}}}},$

with G_(δ,σ) _(min) the set of pixels s of the grid G whose distance with respect to the projection L_(XY) of the axis of rotation exceeds a certain threshold δ and such that the empirical variance of the gray levels taken by the pixel s in the sequence of sectional images X₀, . . . , X_(m) exceeds a certain threshold σ_(min) ².

In practice, δ is chosen between 4 and 20 pixels, σ_(min) ² is for example the q-quantile of the empirical variances of the gray levels, calculated for each pixel s of the grid G, taken by the pixel s in the sequence of sectional images X₀, . . . , X_(m) (thereby signifying that the proportion of the pixels s of the grid G, where the calculated empirical variance of the gray levels is less than or equal to σ_(min) ², is substantially equal to q), q equals between 60% and 95% in general.

Limiting the previous regression to the pixels in G_(δ,σ) _(min) makes it possible to improve the robustness of the estimation by using only the symmetrization times for the pixels in G_(δ,σ) _(min) which are in general more reliable.

Step 334 of determining the direction ā furthermore comprises a step 358 of determining the direction ā, on the basis of the projection L_(XY) and of the angle θ. The direction ā of the axis of rotation L is for example given by the vector of spherical coordinates (1,θ,φ), with θ the angle between the X axis and the direction b of the projection L_(XY), such that b=π_(XY)(ā)/∥π_(XY)(ā)∥.

Optional Determination of the Point of Passage of the Axis of Rotation

As an optional supplement, step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) furthermore comprises a step 360 of determining a point of passage u₀ of the axis of rotation L, on the basis of the symmetrization time or times t(s). Preferably, the point of passage u₀ chosen as being the point of intersection between the axis of rotation L and the straight line perpendicular to the XY plane and passing through the point y₀. The point of passage u₀ is in this case given by the following relation:

$u_{0} = {y_{0} + {\frac{z_{0}}{\sin \; \varphi}{{\overset{\rightarrow}{e}}_{3}.}}}$

Determination of the Series of Perturbation Translations

Step 300 of determining the axis of rotation L and the series of perturbation translations T₁ . . . T_(m) furthermore comprises a step 362 of determining the series of perturbation translations T₁ . . . T_(m), carried out in the same manner as in step 330.

More precisely, step 362 of determining the series of perturbation translations T₁ . . . T_(m) comprises a step 364 of determining, in the same manner as in step 324, the reference translation vectors v_(k,k′) of the affine transformations F_(k,k′) whose values minimize the cost function U, with the direction ā of the axis of rotation L and the angular rate τ both known.

By virtue of the knowledge of the direction ā of the axis of rotation, the minimization of U is greatly simplified, by independently minimizing each term χAff(Q_(k,k′)(ā,τ),v_(k,k′),X_(k)), X_(k′)) so as to individually determine each reference translation vector v_(k,k′).

Step 362 of determining the series of perturbation translations T₁ . . . T_(m) furthermore comprises a step 366 of determining the series of perturbation translations T₁ . . . T_(m) and the point of passage u₀ having the values which minimize the cost function K of step 332, knowing the reference translation vectors v_(k,k′).

As a variant, when the optional step 360 is carried out, the minimization of the cost function K is simplified by using the point of passage u₀ determined in this step. Moreover, step 366 amounts to determining the series of perturbation translations T₁ . . . T_(m) having the value which minimizes the cost function K, the point of passage u₀ being known.

Determination of a Three-Dimensional Representation of the Object

With reference to FIG. 10, step 400 of determining the three-dimensional representation V of the object O comprises a step 402 of determining a volume D, included in the volume U of the chamber 20, on which the object O will be “reconstructed”. The volume D comprises the object O (at least in part) and its close environment. For example, the volume D is a parallelepiped.

Step 400 of determining the three-dimensional representation V of the object O furthermore comprises a step 404 of determining a set Ω of points u of the volume D and a value X(u) of each of these points u at a reference instant. Hereinafter, the initial instant t₀ will be chosen as reference instant. The set Ω of points u comprises, in particular, points o of the object O in its position O₀ at the initial instant t₀.

It will be noted that, in a very simple implementation, the set Ω of points u already forms a three-dimensional representation of the object, said representation being given by the constellation of the points of the set Ω.

Step 404 of determining the set of points Ω is carried out on the basis of the positions of the object O with respect to the section plane P at each picture capture instant t₀ . . . t_(m), and of the sequence of sectional images X₀ . . . X_(m).

More precisely, step 404 of determining the set of points Ω comprises a step 406 of calculating, for each sectional image X_(k), the position C_(k)(s) of each pixel s of the sectional image X_(k) at the initial instant t₀, assuming that this pixel belongs to the object O. The calculation is carried out on the basis of the previously determined parameters of the motion of the object O (angular rate τ, axis of rotation L, series of perturbation translations T₁ . . . T_(m)). The position C_(k)(s) of each point u of the set Ω is given for a respective original pixel s of an image X_(k), by:

C _(k)(s)=R _(ā,τ(t) _(k) _(−t) ₀ ₎(π₃ s−u _(0− T) _(k))+u ₀

with T _(k)=R_(ā,τ(t) _(k) _(−t) ₁ ₎T₁+R_(ā,τ(t) _(k) _(−t) ₂ ₎T₂+ . . . +R_(ā,τ(t) _(k) _(−t) _(k−1) ₎T_(k−1)+T_(k), with T ₀=0, R_(ā,τ(t) _(k) _(−t) ₀ ₎ ^(t) is the matrix transpose of R_(ā,τ(t) _(k) _(−t) ₀ ₎. The set Ω consists of the points u of the volume D with position C_(k)(s) for each pixel s and each sectional image X_(k). Thus s and X_(k) are said to be respectively the original pixel and the image of the point u. It is noted that C_(k)(s) is also the position in the reference frame of the object O chosen for the pixel s of the section plane at the instant of capture t_(k) of the sectional image X_(k).

Each of the points u of the set Ω is associated with the value X_(k)(s) of the original pixel s of the original sectional image X_(k): X(u)=X_(k)(S).

It will be noted that the set Ω can thus comprise one and the same point u several times, each time associated with a respective value, these various values stemming from various sectional images.

Step 400 of determining the three-dimensional representation V of the object O furthermore comprises a step 408 of choosing a three-dimensional representation function V_(β) that can be parametrized with parameters β, and an operation Op giving, on the basis of the three-dimensional representation function V_(β), an estimation function {tilde over (X)}=Op(V_(β)) of the value of each point u of the set Ω.

Once the parameters β have been determined, the three-dimensional representation V is given by the three-dimensional representation function V_(β), preferably at any point of the volume D.

First Variant of Three-Dimensional Representation Function V_(β)

With reference to FIG. 11, in a first variant, the three-dimensional representation function V_(β), chosen in the course of step 408, is a decomposition into B-spline functions of degree r with nodes w spaced apart in an equal manner in space:

w=b+(a ₁ k ₁ ,a ₂ k ₂ ,a ₃ k ₃),

with bεR³, and a₁, a₂ and a₃ respectively the sampling increment in the directions X, Y and Z, and k₁, k₂, k₃ integers. Each parameter of the three-dimensional representation function V_(β) is associated with a respective node.

The three-dimensional representation function V_(β) may then be written:

${{\forall u} = {\left( {u_{1},u_{2},u_{3}} \right) \in D}},\mspace{14mu} {{V(u)} = {\sum\limits_{w = {{({w_{1},w_{2},w_{3}})} \in W}}{{\eta \left( \frac{u_{1} - w_{1}}{a_{1}} \right)}{\eta \left( \frac{u_{2} - w_{2}}{a_{2}} \right)}{\eta \left( \frac{u_{3} - w_{3}}{a_{3}} \right)}{\beta (w)}}}},$

with η the central B-spline function of degree r defined on the set of real numbers R, W the set of nodes in the volume D. η is for example the indicator function on the interval

$\left\lbrack {{- \frac{1}{2}},\frac{1}{2}} \right\rbrack,$

convolved r times with itself:

${\eta (x)} = {\underset{\underset{r + {1\mspace{14mu} {times}}}{}}{1_{\lbrack{{- \frac{1}{2}},\frac{1}{2}}\rbrack}*1_{\lbrack{{- \frac{1}{2}},\frac{1}{2}}\rbrack}*\ldots \mspace{14mu}*1_{\lbrack{{- \frac{1}{2}},\frac{1}{2}}\rbrack}}{(x).}}$

In particular, if r=3 (cubic B-spline function):

${\eta (x)} = \left\{ \begin{matrix} {{\frac{2}{3} - x^{2} + \frac{{x}^{3}}{2}},} & {{{if}\mspace{14mu} 0} \leq {x} < 1} \\ {\frac{\left( {2 - {x}} \right)^{3}}{6},} & {{{if}\mspace{14mu} 1} \leq {x} < 2} \\ {0,} & {{{if}\mspace{14mu} {x}} \geq 2.} \end{matrix} \right.$

In this first variant, the operation Op is chosen as the identity function, so that the estimation function {tilde over (X)} for the value of each point u of the set Ω is equal to the three-dimensional representation function V_(β): {tilde over (X)}(u)=V_(β)(u).

With this choice of three-dimensional representation function V_(β), step 400 of determining the three-dimensional representation V of the object O furthermore comprises a step 410 of dividing the volume D into a plurality of mutually disjoint sub-volumes D_(i). As a variant, the edges of the sub-volumes D_(i) overlap. In this way, the nodes w are also divided into groups {w}, each associated with a respective sub-volume D_(i). Likewise, the points of the set Ω are divided into groups Ω_(i) each associated with a respective sub-volume D_(i).

More precisely, each group {w}, comprises the nodes situated in the respective sub-volume D_(i) and the parameters {β}_(i) of these nodes {w}, are thus associated with the sub-volume D_(i). Likewise, the points of each group Ω_(i), are the points of the set Ω situated in the sub-volume D_(i).

Step 400 of determining the three-dimensional representation V of the object O furthermore comprises a step 412 of determining the parameters β, such that, for each point u of the set Ω, the estimation {tilde over (X)}(u) of the value of the point u gives substantially the value X(u) of the point u.

More precisely, step 412 of determining the parameters β comprises, for each sub-volume D_(i), a step 414 of determining the parameters {β}_(i) associated with this sub-volume D_(i), such that, for each point u of the group Ω_(i) and of the groups directly contiguous with the group Ω_(i), the estimation {tilde over (X)}(u) of the value of the point u gives substantially the value X(u) of the point u, the parameters {β}_(j≠i) associated with the other subsets D_(j≠i) being fixed at a given value.

Step 414 of determining the parameters {β}_(i) is implemented several times, in an iterative manner, each time sweeping all the sub-volumes D_(i): in the course of the first iteration, each group of parameters {β}_(i) is determined successively (for the determination of a group of parameters {β}_(i), the given value of each of the other groups of parameters {β}_(j≠i) is fixed at a predetermined value, for example zero); in the course of the subsequent iterations, each group of parameters {β}_(i) is determined successively (for the determination of a group of parameters {β}_(i), the given value of each of the other groups of parameters {β}_(j≠i) is the last result determined beforehand).

Preferably, the parameters {β}_(i) are determined by minimizing the following cost function:

${{U(\beta)} = {{\sum\limits_{u \in \Omega}\left( {{\overset{\sim}{X}(u)} - {X(u)}} \right)^{2}} + {{\lambda\beta}^{t}A\; \beta}}},$

A is a positive definite symmetric matrix, or more generally semi-positive definite, β^(t) Aβ is a measure of the quadratic irregularity, and λ>0 is a compromise between the fitness of the three-dimensional representation function and regularity.

Second Variant of Three-Dimensional Representation Function V_(β)

With reference to FIG. 12, the three-dimensional representation function V_(β), chosen in the course of step 408, may be written in the form of a decomposition into Radial Basis Functions (RBF) φ with the nodes w:

${\forall{u \in D}},\mspace{14mu} {{V_{\beta}(u)} = {\sum\limits_{w \in W}{{\varphi \left( {u - w} \right)}{\beta (w)}}}},$

with W the set of nodes in the volume D.

The function φ(u−w) depends on the distance between the point u and the node w, but not on the direction between the point u and the node iv. For example, φ(x)=exp(−c∥x∥²) or φ(x)=η(∥x∥) with η the cubic central B-spline function.

Furthermore, the operation Op gives an estimation function {tilde over (X)}=Op(V_(β),f_(R)) for the value of each point u of the set Ω on the basis of the three-dimensional representation function V_(β) and a point spread function f_(R). In the example described, the operation Op is a convolution of the three-dimensional representation function V_(β) with the following point spread function f_(R): f_(R)(u)=f(Ru), with f the point spread function without rotation, which is known (for example, given by the constructor of the imaging system, or determined experimentally), f_(R) the point spread function for the rotation R, and Ru the point resulting from the rotation of the point u by the rotation R.

The point spread function f_(R) depends on the rotation R between the position of the object O at the instant t_(k) of capture of the respective sectional image X_(k) associated with the point u, and the position of the object O at the reference instant t₀: R=R_(ā,τ(t) _(k) _(−t) ₀ ₎. Thus, for each point u of the set Ω,

{tilde over (X)}(u)=Op(V _(β) ,f _(R))(u)=(V _(β) *f _(R))(C _(k)(s)),

with * the convolution operation, the pixel s and the sectional image X_(k) (of the instant of capture t_(k)) being respectively the original pixel and the image of the point u.

On account of the choice of radial basis functions φ, we obtain the property that, for each point u of the volume D:

Op(φ,f _(R))(u)=Op(φ,f)(Ru),

i.e.:

(φ*f _(R))(u)=(φ*f)(Ru)

with * the convolution operation, R an arbitrary rotation.

Thus, step 400 of determining the three-dimensional representation V of the object O furthermore comprises a step 416 of determining the parameters β of the three-dimensional representation function V_(β) by minimizing the following cost function:

${{E(\beta)} = {{\sum\limits_{u \in \Omega}\left( {{X(u)} - {\sum\limits_{w \in W}{\left( {\varphi*f} \right)\left( {{\pi_{3}s} - \varsigma_{i} - {R_{i}w}} \right){\beta (w)}}}} \right)^{2}} + {{\lambda\beta}^{t}A\; \beta}}},$

where s and the sectional image X_(i) the instant of capture t_(i)) are respectively the original pixel and the image of the point u, R_(i)=R_(ā,τ(t) _(i) _(−t) _(o) ₎ and ζ_(i)= T _(i)+u₀−R_(ā,τ(t) _(i) _(−t) _(o) ₎u₀.

In an advantageous manner, this minimization is carried out by calculating a unique convolution (the convolution φ*f) and by solving the linear system, which ensues from the calculation of the convolution φ*f, on the parameters β. The parameters β are thus easily determined.

Variant for Determining the Set Ω

FIG. 13 illustrates a variant embodiment of step 404 of determining a set Ω of points u of the volume D and a value X(u) of each of these points u at a reference instant, so as to determine the set of points Ω on the basis of several sequences of sectional images, denoted S_(l) with l=1 . . . I. Each sequence S_(i) is composed of the sectional images X₀ ^(l), X₁ ^(l), . . . , X_(m) _(l) ^(l), at respective instants of picture captures t₀ ^(l), t₁ ^(l), . . . , t_(m) _(l) ^(l).

In this variant, step 404 of determining the set of points Ω comprises a step 420 of determining a three-dimensional representation function V_(l), on a respective sub-volume D_(l), for each sequence S_(i). This step 420 is for example implemented according to the previous steps 408 to 416 for each sequence S_(i).

Each three-dimensional representation function V_(l) gives a representation of the object O in a respective position, denoted O_(l).

Step 404 of determining the set of points Ω furthermore comprises a step 422 of discretizing each sub-volume D_(l) according to a three-dimensional grid G₃. A discretized sub-volume {tilde over (D)}_(l), grouping together the points of the sub-volume D_(l) that are situated on the three-dimensional grid G₃, is thus obtained for each sequence S_(l). We thus obtain:

∀uε{tilde over (D)} _(l) ,{tilde over (V)} _(l)(u)=V _(l)(u).

Preferably, the three-dimensional grid G₃ has a spacing which is less than or equal to that of the grid G of the section plane P.

Step 404 of determining the set of points Ω furthermore comprises a step 424 of determining, for each sequence S_(l), a rotation Q₁ and a translation h_(l) making it possible to substantially place all the positions O_(l) of the representations of the object O in one and the same reference position.

Preferably, the reference position is that of one of the sequences S_(l). Hereinafter, the position O_(l) of the object of the first sequence S_(l) will be the reference position. Thus, a point o of the object O of the position u in O_(l) is at the position Q_(l)u+h_(l) in O_(l), with l≠1.

Step 424 of determining, for each sequence S_(l), a rotation Q_(l) and a translation h_(l) comprises a step 426 of determining a quantile level q, such that substantially the luminous points of the object O have values {tilde over (V)}_(l)(u) that are greater than or equal to the q-quantile ρ_(l)(q), for each discretized subset {tilde over (D)}_(l). For example, the quantile level q is taken between 60% and 95%.

Step 424 of determining, for each sequence S_(l), a rotation Q_(l) and a translation h_(l) furthermore comprises a step 428 of selecting at least three groups g₁ . . . g_(k), preferably four or more, of points of the discretized subset {tilde over (D)}_(l), according to a selection criterion. The selection criterion is the same for all the sequences S_(l). The selection criterion applies in particular to the value of the points of the discretized subset {tilde over (D)}_(l).

By thus applying the same selection criterion for all the sequences S_(l), it is possible to obtain substantially the same points of the object O for all the sequences S_(l), even if these points do not have the same position for the various sequences S_(l).

In a first variant represented in FIG. 13, step 428 of selecting the groups g₁ . . . g_(k), comprises a step 430 of selecting one and the same number n of the most luminous points (having the highest values). Preferably, the number n is the integer closest to min(qn₁, . . . , gn_(l)) (q is expressed as a percentage), with n₁, . . . , n_(l) the numbers of points in the discretized subsets {tilde over (D)}₁ . . . {tilde over (D)}_(l).

Step 428 of selecting the groups g_(l) . . . g_(k) furthermore comprises a step 432 of ranking, for each sequence S_(l), the n most luminous points (selected previously) according to their value, in descending order for example.

Step 428 of selecting the groups g₁ . . . g_(k) furthermore comprises a step 434 of dividing the n ranked most luminous points into k groups g₁ . . . g_(k) of substantially equal size: the points of the group g₁ are more luminous than those of the group g₂, which are more luminous than those of g₃, etc.

In a second variant represented in FIG. 14, step 428 of selecting the groups g₁ . . . g_(k), comprises a step 436 of calculating, for each sequence S_(l), the barycenter b_(l) of the points u of the subset {tilde over (D)}_(l), whose values are greater than the q-quantile ρ_(l)(q). The set of these points will subsequently be denoted {tilde over (D)}_(l,q). For the calculation of the barycenter b_(l), all the points have the same weighting.

Step 428 of selecting the groups g₁ . . . g_(k), furthermore comprises a step 437 of determining the highest distance between the barycenter and the points of all the sets {tilde over (D)}_(l,q). This distance will subsequently be called the radius r.

Preferably, the radius r is given by:

r=min(max{∥u−b ₁ ∥:uε{tilde over (D)} _(l,q)}, . . . , max{∥u−b _(l) ∥:uε{tilde over (D)} _(l,q)})

Step 428 of selecting the groups g₁ . . . g_(k), furthermore comprises a step 438 of dividing the radius r (that is to say of the segment [0,r]) into k segments of equal sizes (seg_(i)=[(k−i)r/k, (k−i+1)r/k], with 1≦i≦k), each group g₁ . . . g_(k) being associated with a respective segment.

More precisely, for each sequence S_(l), each group g_(i) comprises the points u of {tilde over (D)}_(l,q) whose distance from the barycenter b_(l) lies in the associated segment seg_(i).

Returning to FIG. 13, step 424 of determining, for each sequence S_(l), a rotation Q_(l) and a translation h_(l) furthermore comprises a step 440 of determining, for each sequence S_(l), the barycenter ω_(l,i) of the points u of each of the groups g_(i). For the calculation of the barycenters ω_(l,i), the points have identical weightings.

Step 424 of determining, for each sequence S_(l), a rotation Q_(l) and a translation h_(l) furthermore comprises a step 442 of determining the rotation Q_(l) and the translation h_(l) on the basis of the barycenters ω_(l,i).

Preferably, the rotations Q_(l) and the translations h_(l) are determined through the minimization of a cost function:

${\left( {Q_{l},h_{l}} \right) = {\arg \; {\min\limits_{{Q \in {O{(3)}}},\mspace{14mu} {k \in R^{3}}}{\sum\limits_{i = 1}^{k}{{{Q\; \omega_{1,i}} + h - \omega_{l,i}}}^{2}}}}},$

with O(3) the set of 3-by-3 orthogonal matrices.

Preferably, the solution of the previous minimization is obtained by calculating:

$\left\{ {{{{\begin{matrix} {Q_{l} = {P_{l}P_{1}^{t}}} \\ {h_{l} = {{\overset{\_}{\omega}}_{l} - {Q_{i}{\overset{\_}{\omega}}_{1}}}} \end{matrix}.\mspace{14mu} {with}}\mspace{14mu} {\overset{\_}{\omega}}_{1}} = {{\frac{1}{k}{\sum\limits_{i = 1}^{k}{\omega_{1,i}\mspace{14mu} {and}\mspace{14mu} {\overset{\_}{\omega}}_{l}}}} = {\frac{1}{k}{\sum\limits_{i = 1}^{k}\omega_{l,i}}}}},} \right.$

and P₁ and P_(l) obtained by singular value decomposition (SVD) of the matrix M=Σ_(i=1) ^(k)(ω_(1,j)− ω ₁)(ω_(l,i)− ω _(l))^(t), that is to say M=P₁ΛP_(l) ^(i) with P₁ and P_(l) 3-by-3 orthogonal matrices, and Λ a diagonal matrix with non-negative numbers on the diagonal.

Step 404 of determining the set of points Ω furthermore comprises a step 444 of determining the points u of Ω and a value X(u) of each of these points u, on the basis of the sequences of sectional images S_(l) with l=1 . . . I, the rotation matrices Q_(l) and the translations h_(l) being known: the set Ω consists of the points u of the volume D with position

Q_(l) ^(t)R_(ā) _(l) _(,τ) _(l) _((t) _(k) _(l) _(−t) ₀ ^(l) ₎(π₃s−u₀ ^(l)− T _(k) ^(l))+Q_(l) ^(t)(u₀ ^(l)−h_(l)),

for each pixel s of each sectional image X_(k) ^(l) of each sequence S_(l), with τ_(l), ā_(l), u₀ ^(l) and T_(k) ^(l) which are respectively the parameters of the motion for the sequence S_(l), Q₁=I₃ the 3-by-3 identity matrix, h₁=0, T _(k) ^(l)=R_(ā) _(l) _(,τ) _(l) _((t) _(k) ^(t) _(−t) ₁ ^(t) ₎T₁ ^(l)+R_(ā) _(l) _(,τ) _(l) _((t) _(k) ^(t) _(−t) ₂ ^(t) ₎T₂ ^(l)+ . . . +R_(ā) _(l) _(,τ) _(l) _((t) _(k) ^(t) _(−t) _(k−1) ^(t) ₎T_(k−1) ^(l)+T_(k) ^(l) and T ₀ ^(l)=0. Thus s, X_(k) ^(l) and S_(l) are said to be respectively the original pixel, the image and the sequence of the point u.

Each of the points u of the set Ω is associated with the value X_(k) ^(l)(s) of the pixel s of the sectional image X_(k) ^(l) of the sequence S_(l): X(u)=X_(k) ^(l)(s) with s, X_(k) ^(l) and S_(l) being respectively the original pixel, the image and the sequence of the point u.

Taking Account of the Duration of Acquisition

In the course of the capture of the sectional images, the object O moves during the time of exposure to create an image on the focal plane. This engenders a non-negligible additional motion fuzziness (supplementing the fuzziness defined by the point spread function).

This motion fuzziness is, in one embodiment of the invention, taken into account in step 400 of the three-dimensional representation V of the object O, preferably when the exposure time is relatively long with respect to the time necessary to pass from one sectional image X_(k) to the next sectional image X_(k+1).

Thus, with reference to FIG. 15, in a third variant, step 400 of determining the three-dimensional representation V of the object O comprises a step 450 of determining an acquisition interval I_(k)=[t_(k)+δ₀;t_(k)+δ₀+δ] for each sectional image X_(k), defined by a start-of-acquisition instant t_(k)+δ₀ and an end-of-acquisition instant t_(k)+δ₀+δ, the difference giving the duration of acquisition δ. It is assumed that, for 0≦k<m: t_(k)≦t_(k)+δ₀<t_(k)+δ₀+δ≦t_(k+1).

In the example described, the start-of-acquisition instant δ₀ and the duration of acquisition δ are the same for all the sectional images. Nonetheless, the procedure easily extends to the case where the start-of-acquisition instant δ₀ and the duration of acquisition δ vary according to the sectional images.

During exposure, the luminosity level of each point of the focal plane evolves progressively by adding the luminosity level of the points of the route that it travels.

The position of the object O at each instant t_(k) being known (or estimated), step 400 of the three-dimensional representation V of the object O furthermore comprises a step 452 of determining the continuous position of the object O, as a function of time, between the successive instants t_(k) and t_(k+1), and more particularly during the acquisition intervals I_(k).

Thus, step 452 of determining the continuous position of the object O comprises a step 454 of determining a relation between the position ψ_(k,t)(u) at the instant t_(k)+t (between the successive instants t_(k) and t_(k+1)) of a point of the object, which is situated at the position u at the instant t_(k), and the object's motion parameters: angular rate τ, axis of rotation L and series of perturbation translations T₁ . . . T_(m).

Given that the duration between the instants t_(k) and t_(k+1) is short, we consider that the motion is composed approximately of the stable rotation and of a translation, a linear fraction (proportional to time) of the perturbation translation between the instants t_(k) and t_(k+1).

Thus, the position ψ_(k,t)(u) is given by:

${{\psi_{k,t}(u)} = {{R_{\overset{\rightarrow}{a},{\tau \; t}}\left( {u - u_{0}} \right)} + u_{0} + {\frac{t}{t_{k + 1} - t_{k}}T_{k + 1}}}},{{{for}\mspace{14mu} 0} \leq t \leq {t_{k + 1} - t_{k}}},$

with T_(m+1)=0.

It will be noted that ψk,0(u)=u and ψ_(k,t) _(k+1) _(−t) _(k) (u) is the position of the point of the object at the instant t_(k+), starting from the position u at the instant t_(k).

Step 452 of determining the continuous position of the object O furthermore comprises a step 456 of determining a relation between the position C_(k,t)(s) at the initial instant t₀ of the point o of the object, whose position at the instant t_(k)+t is on the pixel s of the section plane P, on the basis of the previous function ψ_(k,j). This position C_(k,t)(s) is given by:

${C_{k,t}(s)} = {{R_{\overset{\rightarrow}{a},{\tau {({t_{k} - t_{0} + t})}}}^{t}\left( {{\pi_{3}s} - u_{0} - {\frac{t}{t_{k + 1} - t_{k}}T_{k + 1}} - {R_{\overset{\rightarrow}{a},{\tau \; t}}{\overset{\_}{T}}_{k}}} \right)} + {u_{0}.}}$

Step 400 of determining the three-dimensional representation V of the object O furthermore comprises a step 460 of choosing the operator Op as being the integral over the acquisition interval I_(k) of the convolution of the three-dimensional representation function V_(β) with the point spread function f_(R).

Step 400 of determining the three-dimensional representation V of the object O furthermore comprises a step 462 of calculating, for each point u of the set Ω, an estimation {tilde over (X)}(u) of the value of this point u, on the basis of the operator Op. The estimation {tilde over (X)}(u) is given by:

${{\overset{\sim}{X}(u)} = {\int_{= \delta_{0}}^{\delta_{0} + \delta}{V_{\beta}*{f_{R_{\overset{\_}{a},{t{({t_{k} - t_{0} + t})}}}}\ \left( {C_{k,t}(s)} \right)}{t}}}},$

where s and the sectional image X_(k) (of the instant of capture t_(k)) are respectively the original pixel and the image of the point u.

Just as for the second variant, step 400 of determining the three-dimensional representation V of the object O furthermore comprises a step 464 of choosing the three-dimensional representation function V_(β) as a decomposition into radial basis functions φ:

${\forall{u \in D}},\mspace{14mu} {{V_{\beta}(u)} = {\sum\limits_{w \in W}{{\varphi \left( {u - w} \right)}{\beta (w)}}}},$

with W the set of nodes in the volume D.

Step 400 of determining the three-dimensional representation V of the object O furthermore comprises a step 466 of determining the coefficients β(w).

More precisely, by replacing V_(β) by its decomposition into radial basis functions, we obtain, for each point u of the set Ω:

${{\overset{\sim}{X}(u)} = {\sum\limits_{w \in W}{\int_{t = \delta_{0}}^{\delta_{0} + \delta}{{\gamma \begin{pmatrix} {{\pi_{3}s} + {R_{\overset{\rightarrow}{a},{\tau {({t_{k} - t_{0} + t})}}}\left( {u_{0} - w} \right)} -} \\ {{\frac{t}{t_{k + 1} - t_{k}}T_{k + 1}R_{\overset{\rightarrow}{a},{\tau \; t}}{\overset{\_}{T}}_{k}} - u_{0}} \end{pmatrix}}{t}\; {\beta (w)}}}}},$

where s and the sectional image X_(k) (of the instant of capture t_(k)) are respectively the original pixel and the image of the point u, and γ=φ*f.

We write

${\gamma_{k,w}(x)} = {\int_{t = \delta_{0}}^{\delta_{0} + \delta}{{\gamma \begin{pmatrix} {x + {R_{\overset{\rightarrow}{a},{\tau {({t_{k} - t_{0} + t})}}}\left( {u_{0} - w} \right)} -} \\ {{\frac{t}{t_{k + 1} - t_{k}}T_{k + 1}} - {R_{\overset{\rightarrow}{a},{\tau \; t}}{\overset{\_}{T}}_{k}} - u_{0}} \end{pmatrix}}{{t}.}}}$

We therefore have, for each point u of the set Ω:

${{\overset{\sim}{X}(u)} = {\sum\limits_{w \in W}{{\gamma_{k,w}\left( {\pi_{3}s} \right)}{\beta (w)}}}},$

where s and the sectional image X_(k) (of the instant of capture t_(k)) are respectively the original pixel and the image of the point u.

Just as for the second variant y=φ*f is preferably calculated analytically or numerically by approximate calculations.

Thus, step 466 of determining the coefficients β(w) comprises a step of calculating the values γ_(k,w).

If the analytical calculation of γ_(k,w) is unwieldy or impossible, γ_(k,w) is approximated by a discrete sum, for example the Riemann sum:

${{\gamma_{k,w}(x)} \approx {\frac{\delta}{J}{\sum\limits_{j = 0}^{J - 1}{\gamma \begin{pmatrix} {x + {R_{\overset{\rightarrow}{a},{\tau({t_{k} - t_{0} + \delta_{0} + {\delta \frac{j}{J}}})}}\left( {u_{0} - w} \right)} -} \\ {{\frac{\delta_{0} + {\delta \frac{j}{J}}}{t_{k + 1} - t_{k}}T_{k + 1}} - {R_{\overset{\rightarrow}{a},{\tau({\delta_{0} + {\delta \frac{j}{J}}})}}{\overset{\_}{T}}_{k}} - u_{0}} \end{pmatrix}}}}},$

with J a fairly large integer, for example J≈20.

More generally, in the case where several sequences S_(l), l=1, . . . , I are used, the rotations Q_(l) and the translations h_(l) being known, step 466 of determining the coefficients β(w) comprises a step consisting in substantially placing all the positions O_(l) of the representations of the object O in one and the same reference position. Recall that by choosing for example the first sequence as the reference sequence, Q₁=I₃ (with I₃ the 3-by-3 identity matrix) and h₁=0, it is possible to decompose, in the reference frame of the reference sequence, for each point u of the set Ω, the estimation {tilde over (X)}(u) of the value of this point u into a linear combination associated with the coefficients β(w):

$\mspace{20mu} {{{\overset{\sim}{X}(u)} = {\sum\limits_{w \in W}{{\gamma_{k,w}^{l}\left( {\pi_{3}s} \right)}{\beta (w)}}}},\mspace{20mu} {with}}$ ${\gamma_{k,w}^{l}(x)} = {\int_{t = \delta_{0}}^{\delta_{0} + \delta}{{\gamma \begin{pmatrix} {R_{{\overset{\rightarrow}{a}}_{l},{\tau_{l}{({t_{k}^{l} - t_{0}^{l} + t})}}}Q_{l}^{t}{R_{{\overset{\rightarrow}{a}}_{l},{\tau_{l}{({t_{k}^{l} - t_{0}^{l} + t})}}}^{t}\begin{pmatrix} {x - u_{0}^{l} - {\frac{t}{t_{k + 1}^{l} - t_{k}^{l}}T_{k + 1}^{l}} -} \\ {R_{{\overset{\rightarrow}{a}}_{l},{\tau_{l}t}}{\overset{\_}{T}}_{k}^{l}} \end{pmatrix}}} \\ {R_{{\overset{\rightarrow}{a}}_{l},{\tau_{l}{({t_{k}^{l} - t_{0}^{l} + t})}}}\left( {{Q_{l}^{t}\left( {u_{0}^{l} - h_{l}} \right)} - w} \right)} \end{pmatrix}}{t}}}$

where s, the sectional image X_(k) ^(l) (of the instant of capture t_(k) ^(l)) and S_(l) are respectively the original pixel, the image and the sequence of the point u, τ_(l), ā_(l), u₀ ^(l) and T_(k) ^(l) are the parameters of the motion for the sequence S_(l), T_(m) _(l) ₊₁ ^(l)=0 and γ=φ*f.

Thus, in this case, step 466 comprises a step of calculating the values γ_(k,w) ^(l).

Like γ_(k,w)(x), γ_(k,w) ^(l)(x) can also be approximated by a discrete sum (Riemann sum for example).

Preferably, D is chosen sufficiently large such that D contains all the points u with position

Q_(l) ^(t)R_(ā) _(l) _(,τ) _(l) _((t) _(k) _(l) _(−t) ₀ _(l) ₎(π₃s−u₀ ^(l)− T _(k) ^(l))+Q_(l) ^(t)(u₀ ^(l)−h_(l)),

for each pixel s of each sectional image X_(k) ^(l) of each sequence S_(l), with l=1 . . . I. The parameters β of the three-dimensional representation function V_(β) are determined by minimizing the following quadratic cost function:

${{E(\beta)} = {{\sum\limits_{l = 1}^{l}{\sum\limits_{k = 1}^{m_{l}}{\sum\limits_{s}\left( {{X_{k}^{l}(s)} - {\sum\limits_{w \in W}{{\gamma_{k,w}^{l}\left( {\pi_{3}s} \right)}\beta (w)}}} \right)^{2}}}} + {{\gamma\beta}^{t}A\; \beta}}},$

where A is a semi-positive definite matrix, β′ Aβ measures the irregularity of the coefficients β(w), for example A is chosen in the same manner as previously, λ>0 is the parameter of compromise between the fitness of the three-dimensional representation function and the regularity of the coefficients. E(β) may be written in the following matrix form:

E(β)=νy−Kβ∥ ²+λβ^(t) Aβ,

where β is the vector of coefficients, the elements of the vector y are the values X_(k) ^(l)(s), and the matrix K is composed of the elements γ_(k,w) ^(l)(π₃s).

The result of minimizing E(β) is the solution of the following linear system:

(K ^(t) K+λA)β=K ^(t) y.

To calculate the coefficients β(w), it is possible to use numerical optimization procedures, for example the conjugate gradient procedure, or the block-wise optimization procedure presented previously.

Adjustment of the Position of the Section Plane

It is desirable that the largest part of the object O passes within the section plane P. In an optimal configuration, the axis of rotation L is substantially contained in the section plane P. Thus, in one embodiment of the invention, the method comprises a step 53, inserted between steps 52 and 54, of adjusting the position of the section plane P with respect to the axis of rotation L. Quite obviously, the adjustment step can also be implemented independently of steps 50, 52, 54 and 56.

Provision is made for three ways of carrying out the adjustment step 53, so as to obtain substantially the optimal configuration, or else, by default, an appropriate configuration.

First Adjustment Variant

In a first variant, the adjustment step 53 comprises the displacement and the tilting of the optical microscope so as to place the axis of rotation L in the focal plane P. This variant makes it possible to obtain the optimal configuration.

This variant comprises the determination of the axis of rotation L in the same manner as in steps 54 and 56.

Second Adjustment Variant

In a second variant, the adjustment step 53 comprises the modification of the electric or electromagnetic field so as to place the axis of rotation L in the focal plane P. This variant also makes it possible to obtain the optimal configuration.

This variant comprises the determination of the axis of rotation L in the same manner as in steps 54 and 56.

Third Adjustment Variant

With reference to FIG. 16, in a third variant, the adjustment step 53 comprises the displacement of the focal plane P in the direction Z (the direction perpendicular to the focal plane P) so that the focal plane P cuts the object O substantially in the middle of the object. The displacement of the focal plane P is obtained by translating the optical microscope 12, and/or by acting on the lens 14 to change the focal distance.

In the example described, the middle of the object O is taken as being the barycenter of the object O.

Thus, with reference to FIG. 16, the adjustment step 53 comprises first of all a step 53A of capturing a sequence of sectional images X₀ . . . X_(m), and then a step 53B of determining the angular rate τ, the axis of rotation L and the series of perturbation translations T₁ . . . T_(m). Step 53B is implemented in the same manner as step 56.

The adjustment step 53 furthermore comprises a step 53C of determining the set of points Ω in the same manner as in step 404.

The adjustment step 53 furthermore comprises a step 53D of determining a barycenter b of the luminous points of the set Ω. The barycenter b is preferably determined by the relation:

${b = \frac{\sum\limits_{i = 0}^{n}{\sum\limits_{s}{1_{{X_{i}{(s)}} > \alpha}{C_{k}(s)}}}}{\sum\limits_{i = 0}^{n}{\sum\limits_{s}1_{{X_{i}{(s)}} > \alpha}}}},$

with X₀ . . . X_(n), n≦m the part of the sectional images X₀ . . . X_(m), which have been acquired during the time interval in the course of which the object carries out a maximum number of complete revolutions by revolving about the axis of rotation, l_(B>A)=1 when B is greater than A and 0 otherwise, and α is for example the q-quantile of gray level of the sectional images X₀ . . . X_(n) (thereby signifying that the proportion of the pixels which have a gray level less than or equal to α is substantially equal to q). Generally, q equals between 60% and 95%.

The adjustment step 53 furthermore comprises a step 53E of calculating the projection b of the barycenter b onto the axis of rotation L.

The adjustment step 53 furthermore comprises a step 53F of adjusting the imaging system 10 so as to bring the section plane P onto the projection b of the barycenter b.

Annex

Let φ be a radial basis function. For any rotation matrix R, φ(Rx)=φ(x). We then have:

$\begin{matrix} {{\varphi*{f_{R}(x)}} = {\int{{\varphi \left( {x - u} \right)}{f({Ru})}{u}}}} \\ {= {\int{{\varphi \left( {x - {R^{t}y}} \right)}{f(y)}{y}}}} \\ {= {\int{{\varphi \left( {{Rx} - y} \right)}{f(y)}{y}}}} \\ {= {\varphi*{{f({Rx})}.}}} \end{matrix}$ 

1. A method for determining a three-dimensional representation (V) of an object (O), characterized in that it comprises: the determination (404) of a set (Ω) of points (u) of a volume (D) and of a value (X(u)) of each of these points (u) at a given instant (t₀), the set (Ω) of points (u) comprising points (o) of the object (O) in its position at the given instant (t₀), the choosing (408) of a three-dimensional representation function (V_(β)) that can be parametrized with parameters (β), and of an operation (Op) giving, on the basis of the three-dimensional representation function (V_(β)), an estimation function ({tilde over (X)}=Op(V_(β))) for the value of each point (u) of the set (Ω), the determination of parameters (β), such that, for each point (u) of the set (Ω), the estimation ({tilde over (X)}(u)) of the value of the point (u) gives substantially the value of the point (X(u)).
 2. The method as claimed in claim 1, furthermore characterized in that the three-dimensional representation function (V_(β)) comprises a decomposition into basis functions (φ) around nodes (w), so as to obtain a sum of terms, each term comprising the basis function (φ) with a variable (u−w) dependent on a respective node (w) associated with this term.
 3. The method as claimed in claim 2, furthermore characterized in that the basis function (φ) comprises a product of B-spline functions (η) in each of the three directions (X, Y and Z) in space.
 4. The method as claimed in claim 1, furthermore characterized in that: the volume (D) comprises a plurality of sub-volumes (D_(i)), the parameters (β) being distributed in groups of parameters ({β}_(i)), the three-dimensional representation function (V_(β)) is chosen so that each group of parameters ({β}_(i)) is associated with a respective sub-volume (D_(i)), the determination of the parameters (β) comprises, successively for each sub-volume (D_(i)), the determination of the parameters ({β}_(i)) associated with this sub-volume (D_(i)), such that, for each point (u) of the sub-volume (D_(i)), and preferably also of the sub-volumes directly contiguous with the sub-volume (D_(i)), the estimation ({tilde over (X)}(u)) of the value of the point (u) gives substantially the value (X(u)) of the point (u), the parameters {β}_(j≠i) associated with the other sub-volumes (D_(j≠i)) being fixed at a given value.
 5. The method as claimed in claim 2, furthermore characterized in that: the value (X(u)) of each point (u) of the set (Ω) is obtained on the basis of a respective sectional image (X_(k)) of the object, associated with the point (u), the operation (Op) gives a function ({tilde over (X)}=Op(V_(β),f_(R))) for estimating the value of each point (u) of the set (Ω), on the basis of the three-dimensional representation function (V_(β)) and of a point spread function (f_(R)), the point spread function (f_(R)) depending on a rotation (R) between the position of the object (O) at the instant (t_(k)) of capture of the respective sectional image (X_(k)) associated with the point (u), and the position of the object (O) at the given instant (t₀). the three-dimensional representation function (V_(β)) is chosen such that, for each point u of volume (D): Op(φ,f _(R))(u)=Op(φ,f)(Ru), with Op the operation, φ the basis function, R an arbitrary rotation, f_(R) the point spread function for the rotation R, f_(R)(u)=f(Ru), f the point spread function without rotation, and Ru the point resulting from the rotation of the point u by the rotation R.
 6. The method as claimed in claim 5, furthermore characterized in that the operation (Op) is a convolution of the three-dimensional representation function (V_(β)) with the point spread function (f_(R)).
 7. The method as claimed in claim 6, furthermore characterized in that the basis function (φ) is a radial basis function, each term depending on the distance of each point (u) with the node (w) associated with this term, but being independent of the direction between the point (u) and the node (w).
 8. The method as claimed in claim 1, furthermore characterized in that the determination (404) of the set (Ω) of points (u) and of a value (X(u)) of each point (u) is carried out on the basis of several sequences (S_(l)) of sectional images, and in that it comprises: the determination (420) of a three-dimensional representation function (V_(l)), on a respective sub-volume (D_(l)), for each sequence (S_(l)), each three-dimensional representation giving a representation of the object in a respective position, the determination (424), for each sequence (S_(l)), of a rotation (Q_(l)) and of a translation (h^(l)) making it possible to substantially place all the positions (O_(l)) of the representations of the object (O) in a reference position.
 9. The method as claimed in claim 8, furthermore characterized in that the determination (424), for each sequence (S_(l)), of the rotation (Q_(l)) and of the translation (h_(l)) comprises: the selection (428), in each subset (D_(l)), of at least three groups (g₁ . . . g_(k)), preferably four or more, of points of the subset (D_(l)), according to a selection criterion, which is the same for all the sequences (S_(l)) of sectional images, the determination of the rotation (Q_(l)) and of the translation (h_(l)) of each sequence (S_(l)) of sectional images on the basis of the groups of points (g₁ . . . g_(k)).
 10. The method as claimed in claim 9, characterized in that the determination of the rotation (Q_(l)) and of the translation (h_(l)) of each sequence (S_(l)) of sectional images on the basis of the groups of points (g₁ . . . g_(k)) comprises: the calculation (436), for each sequence (S_(l)) of sectional images, of a barycenter of each of the groups of points (g₁ . . . g_(k)), the determination (442) of the rotation (Q_(l)) and of the translation (h_(l)) of each sequence (S_(l)) on the basis of the barycenters.
 11. The method as claimed in claim 1, furthermore characterized in that it comprises: the determination (450) of an interval acquisition (I_(k)=[t_(k)+δ₀;t_(k)δ₀+δ]) of each sectional image (X_(k)), the determination of a continuous motion of the object during the interval of acquisition of each sectional image, the taking into account of the continuous motion of the object during the acquisition interval so as to determine the three-dimensional representation of the object.
 12. The method as claimed in claim 11, furthermore characterized in that: the motion of the object (O) with respect to the section plane (P) comprises a motion of rotation about a fixed axis and with a fixed angular rate and a series of perturbation translations, each undergone by the object between two successive respective sectional images, the continuous motion comprises the rotation about the fixed axis and with the fixed angular rate during the acquisition time for each sectional image, and a linear fraction of the perturbation translation undergone by the object between the sectional image and the next one.
 13. A computer program characterized in that it is designed so as, when implemented on a computer, to implement the steps of a method as claimed in claim
 1. 14. An imaging system characterized in that it comprises: means (12) making it possible to obtain images in a focal plane P, a receptacle (18) for receiving an object (O), means (34, 30) for setting the object (O) into motion, means (36) for receiving sectional images captured in the focal plane, which means are adapted for implementing a method as claimed in claim
 1. 